From fb538a1771b08f105836910d78df932d38da13c4 Mon Sep 17 00:00:00 2001 From: Bjoern Koneswarakantha Date: Wed, 7 Feb 2024 10:58:51 +0100 Subject: [PATCH 1/7] vignette compare with {gsm} --- _pkgdown.yml | 1 + docs/404.html | 5 +- docs/LICENSE-text.html | 5 +- docs/LICENSE.html | 5 +- docs/articles/check_poisson.html | 205 ++- .../figure-html/unnamed-chunk-4-1.png | Bin 80864 -> 80375 bytes .../figure-html/unnamed-chunk-5-1.png | Bin 77500 -> 76263 bytes docs/articles/gsm_perf.html | 1494 +++++++++++++++++ .../gsm_perf_files/figure-html/plot-1.png | Bin 0 -> 114140 bytes .../figure-html/unnamed-chunk-10-1.png | Bin 0 -> 195193 bytes .../figure-html/unnamed-chunk-3-1.png | Bin 0 -> 106592 bytes .../figure-html/unnamed-chunk-9-1.png | Bin 0 -> 212582 bytes docs/articles/index.html | 7 +- docs/articles/intro.html | 10 +- docs/articles/portfolio_perf.html | 397 ++--- .../figure-html/check_portf-1.png | Bin 66381 -> 67478 bytes .../figure-html/check_portf-2.png | Bin 115134 -> 111458 bytes .../figure-html/check_portf_2-1.png | Bin 93592 -> 93275 bytes .../figure-html/check_portf_2-2.png | Bin 41172 -> 41138 bytes docs/articles/sas_files.html | 11 +- docs/articles/usability_limits.html | 5 +- docs/articles/visit_med75.html | 5 +- docs/articles/visits_or_days.html | 9 +- docs/authors.html | 14 +- docs/index.html | 5 +- docs/news/index.html | 9 +- docs/pkgdown.yml | 7 +- docs/reference/aggr_duplicated_visits.html | 5 +- docs/reference/check_df_visit.html | 5 +- docs/reference/eval_sites.html | 51 +- docs/reference/eval_sites_deprecated.html | 30 +- .../exp_implicit_missing_visits.html | 5 +- docs/reference/get_config.html | 130 +- docs/reference/get_ecd_values.html | 43 +- docs/reference/get_pat_pool_config.html | 21 +- docs/reference/get_portf_perf.html | 68 +- docs/reference/get_site_mean_ae_dev.html | 5 +- docs/reference/get_visit_med75.html | 5 +- docs/reference/index.html | 5 +- docs/reference/is_orivisit.html | 5 +- docs/reference/is_simaerep.html | 5 +- docs/reference/orivisit.html | 33 +- docs/reference/pat_aggr.html | 5 +- docs/reference/pat_pool.html | 5 +- docs/reference/pipe.html | 5 +- docs/reference/plot.simaerep.html | 5 +- docs/reference/plot_dots.html | 5 +- docs/reference/plot_sim_example.html | 5 +- docs/reference/plot_sim_examples.html | 5 +- docs/reference/plot_study.html | 5 +- docs/reference/plot_visit_med75.html | 5 +- .../poiss_test_site_ae_vs_study_ae.html | 5 +- docs/reference/prep_for_sim.html | 5 +- .../prob_lower_site_ae_vs_study_ae.html | 5 +- docs/reference/purrr_bar.html | 5 +- docs/reference/sim_after_prep.html | 24 +- docs/reference/sim_scenario.html | 5 +- docs/reference/sim_sites.html | 5 +- docs/reference/sim_studies.html | 34 +- docs/reference/sim_test_data_patient.html | 5 +- docs/reference/sim_test_data_portfolio.html | 68 +- docs/reference/sim_test_data_study.html | 72 +- docs/reference/sim_ur_scenarios.html | 68 +- docs/reference/simaerep.html | 5 +- docs/reference/site_aggr.html | 5 +- docs/reference/with_progress_cnd.html | 5 +- docs/sitemap.xml | 3 + vignettes/check_poisson.Rmd | 6 +- vignettes/gsm_perf.Rmd | 392 +++++ 69 files changed, 2707 insertions(+), 665 deletions(-) create mode 100644 docs/articles/gsm_perf.html create mode 100644 docs/articles/gsm_perf_files/figure-html/plot-1.png create mode 100644 docs/articles/gsm_perf_files/figure-html/unnamed-chunk-10-1.png create mode 100644 docs/articles/gsm_perf_files/figure-html/unnamed-chunk-3-1.png create mode 100644 docs/articles/gsm_perf_files/figure-html/unnamed-chunk-9-1.png create mode 100644 vignettes/gsm_perf.Rmd diff --git a/_pkgdown.yml b/_pkgdown.yml index 082351f..ecae09f 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -36,6 +36,7 @@ articles: - usability_limits - check_poisson - visits_or_days + - gsm_perf reference: - title: S3 Interface diff --git a/docs/404.html b/docs/404.html index 72bd360..698d87f 100644 --- a/docs/404.html +++ b/docs/404.html @@ -91,6 +91,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • @@ -140,7 +143,7 @@

    Page not found (404)

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index 62a479e..0394b8d 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -108,7 +111,7 @@

    License

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/LICENSE.html b/docs/LICENSE.html index a0dfa6c..1d4c349 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -112,7 +115,7 @@

    MIT License

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/articles/check_poisson.html b/docs/articles/check_poisson.html index 029acbf..35d5db9 100644 --- a/docs/articles/check_poisson.html +++ b/docs/articles/check_poisson.html @@ -92,6 +92,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • @@ -198,55 +201,55 @@

    Test Data and Standard Processingdf_sim_sites <- sim_sites(df_site, df_visit) df_visit -
    ## # A tibble: 59,221 × 9
    -##    patnum  site_number is_ur max_visit_mean max_vi…¹ ae_pe…² visit  n_ae study…³
    -##    <chr>   <chr>       <lgl>          <dbl>    <dbl>   <dbl> <int> <int> <chr>  
    -##  1 P000001 S0001       TRUE              20        4    0.25     1     0 ae_per…
    -##  2 P000001 S0001       TRUE              20        4    0.25     2     1 ae_per…
    -##  3 P000001 S0001       TRUE              20        4    0.25     3     2 ae_per…
    -##  4 P000001 S0001       TRUE              20        4    0.25     4     2 ae_per…
    -##  5 P000001 S0001       TRUE              20        4    0.25     5     2 ae_per…
    -##  6 P000001 S0001       TRUE              20        4    0.25     6     2 ae_per…
    -##  7 P000001 S0001       TRUE              20        4    0.25     7     2 ae_per…
    -##  8 P000001 S0001       TRUE              20        4    0.25     8     4 ae_per…
    -##  9 P000001 S0001       TRUE              20        4    0.25     9     4 ae_per…
    -## 10 P000001 S0001       TRUE              20        4    0.25    10     4 ae_per…
    -## # … with 59,211 more rows, and abbreviated variable names ¹​max_visit_sd,
    -## #   ²​ae_per_visit_mean, ³​study_id
    +
    ## # A tibble: 58,187 × 9
    +##    patnum  site_number is_ur max_visit_mean max_visit_sd ae_per_visit_mean visit
    +##    <chr>   <chr>       <lgl>          <dbl>        <dbl>             <dbl> <int>
    +##  1 P000001 S0001       TRUE              20            4              0.25     1
    +##  2 P000001 S0001       TRUE              20            4              0.25     2
    +##  3 P000001 S0001       TRUE              20            4              0.25     3
    +##  4 P000001 S0001       TRUE              20            4              0.25     4
    +##  5 P000001 S0001       TRUE              20            4              0.25     5
    +##  6 P000001 S0001       TRUE              20            4              0.25     6
    +##  7 P000001 S0001       TRUE              20            4              0.25     7
    +##  8 P000001 S0001       TRUE              20            4              0.25     8
    +##  9 P000001 S0001       TRUE              20            4              0.25     9
    +## 10 P000001 S0001       TRUE              20            4              0.25    10
    +## # ℹ 58,177 more rows
    +## # ℹ 2 more variables: n_ae <int>, study_id <chr>
     df_site
    ## # A tibble: 300 × 6
    -##    study_id           site_number n_pat n_pat_with_med75 visit_med75 mean_ae_s…¹
    -##    <chr>              <chr>       <int>            <int>       <dbl>       <dbl>
    -##  1 ae_per_visit: 0.05 S0001          10                8          16       0    
    -##  2 ae_per_visit: 0.05 S0002          10                9          16       0.333
    -##  3 ae_per_visit: 0.05 S0003          10               10          15       0.3  
    -##  4 ae_per_visit: 0.05 S0004          10                9          16       0.111
    -##  5 ae_per_visit: 0.05 S0005          10                9          18       0.556
    -##  6 ae_per_visit: 0.05 S0006          10                8          17       0    
    -##  7 ae_per_visit: 0.05 S0007          10               10          16       0.6  
    -##  8 ae_per_visit: 0.05 S0008          10               10          15       0.5  
    -##  9 ae_per_visit: 0.05 S0009          10                9          17       0.111
    -## 10 ae_per_visit: 0.05 S0010          10               10          16       0.5  
    -## # … with 290 more rows, and abbreviated variable name ¹​mean_ae_site_med75
    +## study_id site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75 +## <chr> <chr> <int> <int> <dbl> <dbl> +## 1 ae_per_vis… S0001 10 9 17 0.333 +## 2 ae_per_vis… S0002 10 9 17 1 +## 3 ae_per_vis… S0003 10 7 19 0.429 +## 4 ae_per_vis… S0004 10 8 18 0.375 +## 5 ae_per_vis… S0005 10 8 17 0.375 +## 6 ae_per_vis… S0006 10 9 16 0.222 +## 7 ae_per_vis… S0007 10 9 16 0.778 +## 8 ae_per_vis… S0008 10 10 14 0.7 +## 9 ae_per_vis… S0009 10 9 16 0.333 +## 10 ae_per_vis… S0010 10 9 15 0.333 +## # ℹ 290 more rows
     df_sim_sites
    ## # A tibble: 300 × 10
    -##    study…¹ site_…² n_pat n_pat…³ visit…⁴ mean_…⁵ mean_…⁶ n_pat…⁷    pval prob_…⁸
    -##    <chr>   <chr>   <int>   <int>   <dbl>   <dbl>   <dbl>   <int>   <dbl>   <dbl>
    -##  1 ae_per… S0001      10       8      16   0       0.699     860 0.00834   0.004
    -##  2 ae_per… S0002      10       9      16   0.333   0.696     859 0.307     0.132
    -##  3 ae_per… S0003      10      10      15   0.3     0.643     898 0.230     0.106
    -##  4 ae_per… S0004      10       9      16   0.111   0.698     859 0.0252    0.016
    -##  5 ae_per… S0005      10       9      18   0.556   0.791     704 0.570     0.28 
    -##  6 ae_per… S0006      10       8      17   0       0.742     787 0.00559   0.007
    -##  7 ae_per… S0007      10      10      16   0.6     0.693     858 1         0.44 
    -##  8 ae_per… S0008      10      10      15   0.5     0.640     898 0.840     0.358
    -##  9 ae_per… S0009      10       9      17   0.111   0.742     786 0.0178    0.013
    -## 10 ae_per… S0010      10      10      16   0.5     0.695     858 0.699     0.304
    -## # … with 290 more rows, and abbreviated variable names ¹​study_id, ²​site_number,
    -## #   ³​n_pat_with_med75, ⁴​visit_med75, ⁵​mean_ae_site_med75, ⁶​mean_ae_study_med75,
    -## #   ⁷​n_pat_with_med75_study, ⁸​prob_low
    +## study_id site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75 +## <chr> <chr> <int> <int> <dbl> <dbl> +## 1 ae_per_vis… S0001 10 9 17 0.333 +## 2 ae_per_vis… S0002 10 9 17 1 +## 3 ae_per_vis… S0003 10 7 19 0.429 +## 4 ae_per_vis… S0004 10 8 18 0.375 +## 5 ae_per_vis… S0005 10 8 17 0.375 +## 6 ae_per_vis… S0006 10 9 16 0.222 +## 7 ae_per_vis… S0007 10 9 16 0.778 +## 8 ae_per_vis… S0008 10 10 14 0.7 +## 9 ae_per_vis… S0009 10 9 16 0.333 +## 10 ae_per_vis… S0010 10 9 15 0.333 +## # ℹ 290 more rows +## # ℹ 4 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>, +## # pval <dbl>, prob_low <dbl>

    Simulate Studies @@ -257,17 +260,9 @@

    Simulate Studies
     set.seed(1)
     
    -future::plan(multiprocess)

    -
    ## Warning: Strategy 'multiprocess' is deprecated in future (>= 1.20.0)
    -## [2020-10-30]. Instead, explicitly specify either 'multisession' (recommended) or
    -## 'multicore'. In the current R session, 'multiprocess' equals 'multisession'.
    -
    ## Warning in supportsMulticoreAndRStudio(...): [ONE-TIME WARNING] Forked
    -## processing ('multicore') is not supported when running R from RStudio
    -## because it is considered unstable. For more details, how to control forked
    -## processing or not, and how to silence this warning in future R sessions,
    -## see ?parallelly::supportsMulticore
    -
    -df_sim_studies <- sim_studies(df_visit, df_site,
    +plan(multisession, workers = 6)
    +
    +df_sim_studies <- sim_studies(df_visit, df_site,
                                   r = 100,
                                   parallel = TRUE,
                                   poisson_test = TRUE,
    @@ -276,20 +271,20 @@ 

    Simulate Studies df_sim_studies

    ## # A tibble: 30,000 × 8
    -##        r study_id           site_number visit_me…¹ n_pat…² n_pat…³  pval prob_…⁴
    -##    <dbl> <chr>              <chr>            <dbl>   <int>   <dbl> <dbl>   <dbl>
    -##  1     1 ae_per_visit: 0.05 S0001               16       8     860 0.389   0.208
    -##  2     1 ae_per_visit: 0.05 S0002               16       9     859 0.841   0.362
    -##  3     1 ae_per_visit: 0.05 S0003               15      10     898 1       1    
    -##  4     1 ae_per_visit: 0.05 S0004               16       9     859 0.672   0.363
    -##  5     1 ae_per_visit: 0.05 S0005               18       9     704 1       1    
    -##  6     1 ae_per_visit: 0.05 S0006               17       8     787 0.543   0.26 
    -##  7     1 ae_per_visit: 0.05 S0007               16      10     858 1       0.48 
    -##  8     1 ae_per_visit: 0.05 S0008               15      10     898 0.105   0.047
    -##  9     1 ae_per_visit: 0.05 S0009               17       9     786 1       0.562
    -## 10     1 ae_per_visit: 0.05 S0010               16      10     858 1       0.576
    -## # … with 29,990 more rows, and abbreviated variable names ¹​visit_med75,
    -## #   ²​n_pat_with_med75, ³​n_pat_study, ⁴​prob_low
    +## r study_id site_number visit_med75 n_pat_with_med75 n_pat_study pval +## <dbl> <chr> <chr> <dbl> <int> <dbl> <dbl> +## 1 1 ae_per_visi… S0001 17 9 738 1 +## 2 1 ae_per_visi… S0002 17 9 738 1 +## 3 1 ae_per_visi… S0003 19 7 571 0.305 +## 4 1 ae_per_visi… S0004 18 8 663 1 +## 5 1 ae_per_visi… S0005 17 8 739 1 +## 6 1 ae_per_visi… S0006 16 9 820 0.571 +## 7 1 ae_per_visi… S0007 16 9 820 0.570 +## 8 1 ae_per_visi… S0008 14 10 918 1 +## 9 1 ae_per_visi… S0009 16 9 820 0.236 +## 10 1 ae_per_visi… S0010 15 9 871 0.839 +## # ℹ 29,990 more rows +## # ℹ 1 more variable: prob_low <dbl>

    Check p-value Probabilities @@ -302,28 +297,27 @@

    Check p-value Probabilities
    +
     df_check_pval <- get_ecd_values(df_sim_studies, df_sim_sites, val_str = "pval")
     
     df_check_pval
    ## # A tibble: 300 × 11
    -##    study…¹ site_…² n_pat n_pat…³ visit…⁴ mean_…⁵ mean_…⁶ n_pat…⁷    pval prob_…⁸
    -##    <chr>   <chr>   <int>   <int>   <dbl>   <dbl>   <dbl>   <int>   <dbl>   <dbl>
    -##  1 ae_per… S0001      10       8      16   0       0.699     860 0.00834   0.004
    -##  2 ae_per… S0002      10       9      16   0.333   0.696     859 0.307     0.132
    -##  3 ae_per… S0003      10      10      15   0.3     0.643     898 0.230     0.106
    -##  4 ae_per… S0004      10       9      16   0.111   0.698     859 0.0252    0.016
    -##  5 ae_per… S0005      10       9      18   0.556   0.791     704 0.570     0.28 
    -##  6 ae_per… S0006      10       8      17   0       0.742     787 0.00559   0.007
    -##  7 ae_per… S0007      10      10      16   0.6     0.693     858 1         0.44 
    -##  8 ae_per… S0008      10      10      15   0.5     0.640     898 0.840     0.358
    -##  9 ae_per… S0009      10       9      17   0.111   0.742     786 0.0178    0.013
    -## 10 ae_per… S0010      10      10      16   0.5     0.695     858 0.699     0.304
    -## # … with 290 more rows, 1 more variable: pval_ecd <dbl>, and abbreviated
    -## #   variable names ¹​study_id, ²​site_number, ³​n_pat_with_med75, ⁴​visit_med75,
    -## #   ⁵​mean_ae_site_med75, ⁶​mean_ae_study_med75, ⁷​n_pat_with_med75_study,
    -## #   ⁸​prob_low
    -
    +##    study_id    site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75
    +##    <chr>       <chr>       <int>            <int>       <dbl>              <dbl>
    +##  1 ae_per_vis… S0001          10                9          17              0.333
    +##  2 ae_per_vis… S0002          10                9          17              1    
    +##  3 ae_per_vis… S0003          10                7          19              0.429
    +##  4 ae_per_vis… S0004          10                8          18              0.375
    +##  5 ae_per_vis… S0005          10                8          17              0.375
    +##  6 ae_per_vis… S0006          10                9          16              0.222
    +##  7 ae_per_vis… S0007          10                9          16              0.778
    +##  8 ae_per_vis… S0008          10               10          14              0.7  
    +##  9 ae_per_vis… S0009          10                9          16              0.333
    +## 10 ae_per_vis… S0010          10                9          15              0.333
    +## # ℹ 290 more rows
    +## # ℹ 5 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>,
    +## #   pval <dbl>, prob_low <dbl>, pval_ecd <dbl>
    +
     df_check_pval %>%
       ggplot(aes(log(pval, base = 10), log(pval_ecd, base = 10))) +
         geom_point(alpha = 0.5, size = 2) +
    @@ -342,28 +336,27 @@ 

    Perform Same Check on

    Note: Dots on the edge of the graph that are cut off have zero value for the corresponding axis. For the tie simulations using 1000 repeats the smallest value greater zero we get for prob_low is 0.001 (1e-3).

    -
    +
     df_check_prob <- get_ecd_values(df_sim_studies, df_sim_sites, val_str = "prob_low")
     
     df_check_prob
    ## # A tibble: 300 × 11
    -##    study…¹ site_…² n_pat n_pat…³ visit…⁴ mean_…⁵ mean_…⁶ n_pat…⁷    pval prob_…⁸
    -##    <chr>   <chr>   <int>   <int>   <dbl>   <dbl>   <dbl>   <int>   <dbl>   <dbl>
    -##  1 ae_per… S0001      10       8      16   0       0.699     860 0.00834   0.004
    -##  2 ae_per… S0002      10       9      16   0.333   0.696     859 0.307     0.132
    -##  3 ae_per… S0003      10      10      15   0.3     0.643     898 0.230     0.106
    -##  4 ae_per… S0004      10       9      16   0.111   0.698     859 0.0252    0.016
    -##  5 ae_per… S0005      10       9      18   0.556   0.791     704 0.570     0.28 
    -##  6 ae_per… S0006      10       8      17   0       0.742     787 0.00559   0.007
    -##  7 ae_per… S0007      10      10      16   0.6     0.693     858 1         0.44 
    -##  8 ae_per… S0008      10      10      15   0.5     0.640     898 0.840     0.358
    -##  9 ae_per… S0009      10       9      17   0.111   0.742     786 0.0178    0.013
    -## 10 ae_per… S0010      10      10      16   0.5     0.695     858 0.699     0.304
    -## # … with 290 more rows, 1 more variable: prob_low_ecd <dbl>, and abbreviated
    -## #   variable names ¹​study_id, ²​site_number, ³​n_pat_with_med75, ⁴​visit_med75,
    -## #   ⁵​mean_ae_site_med75, ⁶​mean_ae_study_med75, ⁷​n_pat_with_med75_study,
    -## #   ⁸​prob_low
    -
    +##    study_id    site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75
    +##    <chr>       <chr>       <int>            <int>       <dbl>              <dbl>
    +##  1 ae_per_vis… S0001          10                9          17              0.333
    +##  2 ae_per_vis… S0002          10                9          17              1    
    +##  3 ae_per_vis… S0003          10                7          19              0.429
    +##  4 ae_per_vis… S0004          10                8          18              0.375
    +##  5 ae_per_vis… S0005          10                8          17              0.375
    +##  6 ae_per_vis… S0006          10                9          16              0.222
    +##  7 ae_per_vis… S0007          10                9          16              0.778
    +##  8 ae_per_vis… S0008          10               10          14              0.7  
    +##  9 ae_per_vis… S0009          10                9          16              0.333
    +## 10 ae_per_vis… S0010          10                9          15              0.333
    +## # ℹ 290 more rows
    +## # ℹ 5 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>,
    +## #   pval <dbl>, prob_low <dbl>, prob_low_ecd <dbl>
    +
     df_check_prob %>%
       ggplot(aes(log(prob_low, base = 10), log(prob_low_ecd, base = 10))) +
         geom_point(alpha = 0.5, size = 2) +
    @@ -387,6 +380,8 @@ 

    Conclusion
    +plan(sequential)

    @@ -408,7 +403,7 @@

    Conclusion

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

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  • Changelog @@ -107,6 +110,8 @@

    Articles

    Aggregate AEs by Days or Visit?
    +
    Comparing {simaerep} and {gsm} Performance
    +
    @@ -117,7 +122,7 @@

    Articles

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/articles/intro.html b/docs/articles/intro.html index 21c3bf2..f9e1a0a 100644 --- a/docs/articles/intro.html +++ b/docs/articles/intro.html @@ -92,6 +92,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • @@ -965,7 +968,10 @@

    Applying {simaerep}## Please use `all.equal()` instead. ## And manually order the rows/cols as needed ## The deprecated feature was likely used in the simaerep package. -## Please report the issue to the authors. +## Please report the issue to the authors. +## This warning is displayed once every 8 hours. +## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was +## generated.

    For modifying the default parameters please check out the simaerep() documentation.

    @@ -989,7 +995,7 @@

    Applying {simaerep}

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/articles/portfolio_perf.html b/docs/articles/portfolio_perf.html index aac6361..0713eb5 100644 --- a/docs/articles/portfolio_perf.html +++ b/docs/articles/portfolio_perf.html @@ -92,6 +92,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • @@ -243,162 +246,162 @@

    Portfolio Configuration 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 10 0001 -0.3582915 +0.4096706 0002 -4.813176 -20.5 +2.898275 +17.8 10 0001 -0.3582915 +0.4096706 0003 -4.690416 -20.0 +2.065591 +17.4 10 0001 -0.3582915 +0.4096706 0004 -2.378141 -21.9 +4.341019 +21.8 10 0001 -0.3582915 +0.4096706 0005 -4.478343 -19.5 +2.359378 +20.3 10 0001 -0.3582915 +0.4096706 0006 -3.198958 -19.3 +4.516636 +21.2 10 0001 -0.3582915 +0.4096706 0007 -2.503331 -19.6 +3.627059 +16.6 10 0001 -0.3582915 +0.4096706 0008 -2.213594 -20.3 +2.973961 +17.8 10 0001 -0.3582915 +0.4096706 0009 -5.391351 -18.8 +2.319004 +16.4 10 0001 -0.3582915 +0.4096706 0010 -3.695342 -19.9 +5.028806 +19.8 10 0002 -0.4827586 +0.4755621 0001 -3.675746 -18.8 +6.929005 +19.3 10 0002 -0.4827586 +0.4755621 0002 -6.168018 -19.6 +3.233505 +19.3 10 0002 -0.4827586 +0.4755621 0003 -3.717825 -19.4 +4.623611 +20.4 10 0002 -0.4827586 +0.4755621 0004 -4.137901 -18.3 +3.881580 +21.2 10 0002 -0.4827586 +0.4755621 0005 -3.705851 -21.2 +6.019413 +19.3 10 0002 -0.4827586 +0.4755621 0006 -3.178050 -18.1 +3.921451 +21.6 10 0002 -0.4827586 +0.4755621 0007 -5.103376 -19.4 +6.963396 +20.6 10 0002 -0.4827586 +0.4755621 0008 -3.472111 -19.5 +4.894441 +21.8 10 0002 -0.4827586 +0.4755621 0009 -2.677063 -19.5 +3.333333 +21.0 10 0002 -0.4827586 +0.4755621 0010 -2.460804 -20.5 +2.469818 +20.1 10 @@ -439,253 +442,253 @@

    Simulate Portfolio from Configura 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0001 1 -0 +1 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0001 2 -0 +1 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0001 3 -0 +1 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0001 4 1 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0001 5 -2 +1 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0001 6 -3 +2 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0001 7 -4 +2 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0001 8 -4 +2 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0001 9 -5 +2 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0001 10 -5 +3 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0001 11 -5 +3 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0001 12 -6 +4 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0001 13 -6 +4 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0001 14 -6 +4 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0001 15 -6 +4 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0001 16 -6 +5 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 -0002 -1 -0 +3.573047 +19.1 +0001 +17 +7 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 -0002 -2 -2 +3.573047 +19.1 +0001 +18 +7 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 -0002 -3 -4 +3.573047 +19.1 +0001 +19 +8 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0002 -4 -4 +1 +0 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0002 -5 -4 +2 +1 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0002 -6 -4 +3 +1 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0002 -7 4 +2 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0002 -8 -6 +5 +2 0001 -0.3582915 +0.4096706 0001 -3.119829 -19.2 +3.573047 +19.1 0002 -9 6 +4 @@ -1080,7 +1083,7 @@

    Simulate Portfolio622 0001 3 -0 +1 0001 @@ -1132,7 +1135,7 @@

    Simulate Portfolio622 0001 7 -1 +2 0001 @@ -1236,7 +1239,7 @@

    Simulate Portfolio622 0001 15 -3 +4 0001 @@ -1249,7 +1252,7 @@

    Simulate Portfolio622 0001 16 -3 +5 0001 @@ -1262,7 +1265,7 @@

    Simulate Portfolio622 0001 17 -3 +5 0001 @@ -1275,7 +1278,7 @@

    Simulate Portfolio622 0001 18 -3 +5 0001 @@ -1288,7 +1291,7 @@

    Simulate Portfolio622 0001 19 -3 +5 0001 @@ -1301,7 +1304,7 @@

    Simulate Portfolio622 0001 20 -3 +5 0001 @@ -1314,7 +1317,7 @@

    Simulate Portfolio622 0001 21 -3 +6 0001 @@ -1327,7 +1330,7 @@

    Simulate Portfolio622 0002 1 -1 +0 0001 @@ -2357,7 +2360,7 @@

    Performance Under Optimal Conditio ## 8 0 8 <tibble [3,869 × 8]> ## 9 0 9 <tibble [3,884 × 8]> ## 10 0 10 <tibble [3,884 × 8]> -## # … with 2,990 more rows +## # ℹ 2,990 more rows
     df_visit <- df_data_grid %>%
       mutate(
    @@ -4407,17 +4410,17 @@ 

    Heuristic Rank## # A tibble: 15,674 × 6 ## study_id site_number visit n_ae ae_per_visit ls_study_ae_per_visit ## <chr> <chr> <int> <int> <dbl> <list> -## 1 0001 0001 44 8 0.182 <dbl [43]> -## 2 0001 0002 49 18 0.367 <dbl [43]> -## 3 0001 0003 74 17 0.230 <dbl [43]> -## 4 0001 0004 191 49 0.257 <dbl [43]> -## 5 0001 0005 30 9 0.3 <dbl [43]> -## 6 0001 0006 35 17 0.486 <dbl [43]> -## 7 0001 0007 36 9 0.25 <dbl [43]> -## 8 0001 0008 166 48 0.289 <dbl [43]> -## 9 0001 0009 40 14 0.35 <dbl [43]> -## 10 0001 0010 138 33 0.239 <dbl [43]> -## # … with 15,664 more rows

    +## 1 0001 0001 42 11 0.262 <dbl [43]> +## 2 0001 0002 55 15 0.273 <dbl [43]> +## 3 0001 0003 74 24 0.324 <dbl [43]> +## 4 0001 0004 170 37 0.218 <dbl [43]> +## 5 0001 0005 30 10 0.333 <dbl [43]> +## 6 0001 0006 35 6 0.171 <dbl [43]> +## 7 0001 0007 36 14 0.389 <dbl [43]> +## 8 0001 0008 206 62 0.301 <dbl [43]> +## 9 0001 0009 40 18 0.45 <dbl [43]> +## 10 0001 0010 141 34 0.241 <dbl [43]> +## # ℹ 15,664 more rows

    We write a function that: - determines how many sites should be flagged in a study - pools ae_per_visit rates and ranks sites (using dense_rank()) - site gets flagged if the specific rank for a site is @@ -4472,11 +4475,11 @@

    Heuristic Rank## # A tibble: 6 × 3 ## ur_rate ae_per_visit is_ur ## <dbl> <dbl> <lgl> -## 1 0 0.182 FALSE -## 2 0.1 0.164 FALSE -## 3 0.25 0.136 TRUE -## 4 0.5 0.0909 TRUE -## 5 0.75 0.0455 TRUE +## 1 0 0.262 FALSE +## 2 0.1 0.236 FALSE +## 3 0.25 0.196 FALSE +## 4 0.5 0.131 TRUE +## 5 0.75 0.0655 TRUE ## 6 1 0 TRUE

    We apply.

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  • @@ -165,7 +168,7 @@

    SAS files## 8 CO-101-001 01001001 001 99 ## 9 CO-101-001 01001001 001 127 ## 10 CO-101-001 01001001 001 127 -## # … with 4,692 more rows +## # ℹ 4,692 more rows

  • @@ -553,7 +556,7 @@

    Conclusion

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/articles/visit_med75.html b/docs/articles/visit_med75.html index 30c8979..bd96736 100644 --- a/docs/articles/visit_med75.html +++ b/docs/articles/visit_med75.html @@ -92,6 +92,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • @@ -262,7 +265,7 @@

    Early Starters

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/articles/visits_or_days.html b/docs/articles/visits_or_days.html index c1b0568..2548575 100644 --- a/docs/articles/visits_or_days.html +++ b/docs/articles/visits_or_days.html @@ -92,6 +92,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • @@ -297,7 +300,7 @@

    Aggregate on Days## 8 CO-101-001 001 01001001 184 10 ## 9 CO-101-001 001 01001001 240 13 ## 10 CO-101-001 001 01001001 241 14 -## # … with 3,210 more rows +## # ℹ 3,210 more rows

    We do have gaps in between the days leading to implicitly missing values. simaerep will correct this automatically and throw a warning.

    @@ -327,7 +330,7 @@

    Aggregate on Days## 8 CO-101-001 001 01001001 12 0 ## 9 CO-101-001 001 01001001 13 0 ## 10 CO-101-001 001 01001001 14 0 -## # … with 57,863 more rows +## # ℹ 57,863 more rows

    Then we proceed as usual.

     df_sim_sites <- sim_sites(df_site, df_visit = df_days)
    @@ -530,7 +533,7 @@ 

    Compare

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/authors.html b/docs/authors.html index f05bf46..dd05dfb 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -108,15 +111,16 @@

    Citation

    -

    Koneswarakantha B (2023). +

    Koneswarakantha B (2024). simaerep: Find Clinical Trial Sites Under-Reporting Adverse Events. -https://openpharma.github.io/simaerep/, https://github.com/openpharma/simaerep. +R package version 0.4.3, https://github.com/openpharma/simaerep, https://openpharma.github.io/simaerep/.

    @Manual{,
       title = {simaerep: Find Clinical Trial Sites Under-Reporting Adverse Events},
       author = {Bjoern Koneswarakantha},
    -  year = {2023},
    -  note = {https://openpharma.github.io/simaerep/, https://github.com/openpharma/simaerep},
    +  year = {2024},
    +  note = {R package version 0.4.3, https://github.com/openpharma/simaerep},
    +  url = {https://openpharma.github.io/simaerep/},
     }
    @@ -130,7 +134,7 @@

    Citation

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/index.html b/docs/index.html index 2516384..8ac4dc6 100644 --- a/docs/index.html +++ b/docs/index.html @@ -96,6 +96,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • @@ -459,7 +462,7 @@

    Dev status

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/news/index.html b/docs/news/index.html index 827a262..c564c0b 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -91,8 +94,8 @@

    Changelog

    - -
    • skip all performance unit tests on CRAN
    • + +
      • delete performance unit tests (poisson faster than bootstrap) to accommodate CRAN request
    @@ -146,7 +149,7 @@
    diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 1670938..3b6a57d 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,15 +1,16 @@ -pandoc: 3.0.1 -pkgdown: 2.0.5 +pandoc: 3.1.1 +pkgdown: 2.0.7 pkgdown_sha: ~ articles: check_poisson: check_poisson.html + gsm_perf: gsm_perf.html intro: intro.html portfolio_perf: portfolio_perf.html sas_files: sas_files.html usability_limits: usability_limits.html visit_med75: visit_med75.html visits_or_days: visits_or_days.html -last_built: 2023-02-28T09:05Z +last_built: 2024-02-07T09:37Z urls: reference: https://openpharma.github.io/simaerep/reference article: https://openpharma.github.io/simaerep/articles diff --git a/docs/reference/aggr_duplicated_visits.html b/docs/reference/aggr_duplicated_visits.html index 48f1dff..5ac3d04 100644 --- a/docs/reference/aggr_duplicated_visits.html +++ b/docs/reference/aggr_duplicated_visits.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -125,7 +128,7 @@

    Value

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/check_df_visit.html b/docs/reference/check_df_visit.html index 674d749..710756d 100644 --- a/docs/reference/check_df_visit.html +++ b/docs/reference/check_df_visit.html @@ -69,6 +69,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -158,7 +161,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/eval_sites.html b/docs/reference/eval_sites.html index 3526a30..564f89b 100644 --- a/docs/reference/eval_sites.html +++ b/docs/reference/eval_sites.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -174,36 +177,34 @@

    Examples

    df_eval <- eval_sites(df_sim_sites) df_eval #> # A tibble: 5 × 14 -#> study_id site_…¹ n_pat n_pat…² visit…³ mean_…⁴ mean_…⁵ n_pat…⁶ pval prob_…⁷ -#> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl> -#> 1 A S0002 20 17 15 3 6.48 69 1.43e-8 0 -#> 2 A S0001 20 16 15 2.94 6.44 70 1.84e-8 0 -#> 3 A S0003 20 18 16 8.06 5.80 64 1 e+0 1 -#> 4 A S0004 20 19 16 7.63 5.89 63 1 e+0 1 -#> 5 A S0005 20 16 15 8.38 5.2 70 1 e+0 1 -#> # … with 4 more variables: pval_adj <dbl>, pval_prob_ur <dbl>, -#> # prob_low_adj <dbl>, prob_low_prob_ur <dbl>, and abbreviated variable names -#> # ¹​site_number, ²​n_pat_with_med75, ³​visit_med75, ⁴​mean_ae_site_med75, -#> # ⁵​mean_ae_study_med75, ⁶​n_pat_with_med75_study, ⁷​prob_low +#> study_id site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75 +#> <chr> <chr> <int> <int> <dbl> <dbl> +#> 1 A S0002 20 16 16 2.75 +#> 2 A S0001 20 16 15 2.62 +#> 3 A S0003 20 19 15 6.37 +#> 4 A S0004 20 16 16 7.81 +#> 5 A S0005 20 17 16 8.59 +#> # ℹ 8 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>, +#> # pval <dbl>, prob_low <dbl>, pval_adj <dbl>, pval_prob_ur <dbl>, +#> # prob_low_adj <dbl>, prob_low_prob_ur <dbl> # use deprecated method ------- df_eval <- eval_sites(df_sim_sites, method = NULL, r_sim_sites = 100) #> Warning: using deprecated method for probability adjustment df_eval #> # A tibble: 5 × 19 -#> study_id site_…¹ n_pat n_pat…² visit…³ mean_…⁴ mean_…⁵ n_pat…⁶ pval prob_…⁷ -#> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl> -#> 1 A S0002 20 17 15 3 6.48 69 1.43e-8 0 -#> 2 A S0001 20 16 15 2.94 6.44 70 1.84e-8 0 -#> 3 A S0003 20 18 16 8.06 5.80 64 1 e+0 1 -#> 4 A S0004 20 19 16 7.63 5.89 63 1 e+0 1 -#> 5 A S0005 20 16 15 8.38 5.2 70 1 e+0 1 -#> # … with 9 more variables: n_site <int>, pval_n_detected <int>, pval_fp <dbl>, -#> # pval_p_vs_fp_ratio <dbl>, pval_prob_ur <dbl>, prob_low_n_detected <int>, -#> # prob_low_fp <dbl>, prob_low_p_vs_fp_ratio <dbl>, prob_low_prob_ur <dbl>, -#> # and abbreviated variable names ¹​site_number, ²​n_pat_with_med75, -#> # ³​visit_med75, ⁴​mean_ae_site_med75, ⁵​mean_ae_study_med75, -#> # ⁶​n_pat_with_med75_study, ⁷​prob_low +#> study_id site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75 +#> <chr> <chr> <int> <int> <dbl> <dbl> +#> 1 A S0002 20 16 16 2.75 +#> 2 A S0001 20 16 15 2.62 +#> 3 A S0003 20 19 15 6.37 +#> 4 A S0004 20 16 16 7.81 +#> 5 A S0005 20 17 16 8.59 +#> # ℹ 13 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>, +#> # pval <dbl>, prob_low <dbl>, n_site <int>, pval_n_detected <int>, +#> # pval_fp <dbl>, pval_p_vs_fp_ratio <dbl>, pval_prob_ur <dbl>, +#> # prob_low_n_detected <int>, prob_low_fp <dbl>, prob_low_p_vs_fp_ratio <dbl>, +#> # prob_low_prob_ur <dbl>
    @@ -218,7 +219,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/eval_sites_deprecated.html b/docs/reference/eval_sites_deprecated.html index eff3395..5f46697 100644 --- a/docs/reference/eval_sites_deprecated.html +++ b/docs/reference/eval_sites_deprecated.html @@ -69,6 +69,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -192,19 +195,18 @@

    Examples

    df_eval <- eval_sites_deprecated(df_sim_sites, r_sim_sites = 100) df_eval #> # A tibble: 5 × 19 -#> study…¹ site_…² n_pat n_pat…³ visit…⁴ mean_…⁵ mean_…⁶ n_pat…⁷ pval prob_…⁸ -#> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl> -#> 1 A S0001 20 20 16 3.25 7.18 68 5.77e-11 0 -#> 2 A S0002 20 17 18 3.88 7.81 58 1.20e- 8 0 -#> 3 A S0003 20 18 17 10 5.78 64 1 e+ 0 1 -#> 4 A S0004 20 18 14 7.39 5.01 76 1 e+ 0 1 -#> 5 A S0005 20 18 15 7.28 5.51 72 1 e+ 0 1 -#> # … with 9 more variables: n_site <int>, pval_n_detected <int>, pval_fp <dbl>, -#> # pval_p_vs_fp_ratio <dbl>, pval_prob_ur <dbl>, prob_low_n_detected <int>, -#> # prob_low_fp <dbl>, prob_low_p_vs_fp_ratio <dbl>, prob_low_prob_ur <dbl>, -#> # and abbreviated variable names ¹​study_id, ²​site_number, ³​n_pat_with_med75, -#> # ⁴​visit_med75, ⁵​mean_ae_site_med75, ⁶​mean_ae_study_med75, -#> # ⁷​n_pat_with_med75_study, ⁸​prob_low +#> study_id site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75 +#> <chr> <chr> <int> <int> <dbl> <dbl> +#> 1 A S0002 20 17 18 3.94 +#> 2 A S0001 20 18 16 3.39 +#> 3 A S0003 20 18 17 10.2 +#> 4 A S0004 20 18 14 7.28 +#> 5 A S0005 20 17 15 7.29 +#> # ℹ 13 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>, +#> # pval <dbl>, prob_low <dbl>, n_site <int>, pval_n_detected <int>, +#> # pval_fp <dbl>, pval_p_vs_fp_ratio <dbl>, pval_prob_ur <dbl>, +#> # prob_low_n_detected <int>, prob_low_fp <dbl>, prob_low_p_vs_fp_ratio <dbl>, +#> # prob_low_prob_ur <dbl> @@ -219,7 +221,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/exp_implicit_missing_visits.html b/docs/reference/exp_implicit_missing_visits.html index 54f04cc..b2eaef7 100644 --- a/docs/reference/exp_implicit_missing_visits.html +++ b/docs/reference/exp_implicit_missing_visits.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -125,7 +128,7 @@

    Value

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/get_config.html b/docs/reference/get_config.html index fd3510f..d721ffd 100644 --- a/docs/reference/get_config.html +++ b/docs/reference/get_config.html @@ -71,6 +71,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -193,45 +196,45 @@

    Examples

    #> # A tibble: 20 × 6 #> study_id ae_per_visit_mean site_number max_visit_sd max_visit_mean n_pat #> <chr> <dbl> <chr> <dbl> <dbl> <int> -#> 1 0001 0.401 0001 3.41 20.1 10 -#> 2 0001 0.401 0002 2.33 21.1 10 -#> 3 0001 0.401 0003 3.97 20.2 10 -#> 4 0001 0.401 0004 3.30 21.3 10 -#> 5 0001 0.401 0005 4.25 20.9 10 -#> 6 0001 0.401 0006 4.50 19.5 10 -#> 7 0001 0.401 0007 3.92 17.3 10 -#> 8 0001 0.401 0008 4.76 19.7 10 -#> 9 0001 0.401 0009 4.40 19.3 10 -#> 10 0001 0.401 0010 4.27 19.3 10 -#> 11 0002 0.487 0001 2.99 20.4 10 -#> 12 0002 0.487 0002 4.30 19.7 10 -#> 13 0002 0.487 0003 2.49 19.8 10 -#> 14 0002 0.487 0004 5.32 19.4 10 -#> 15 0002 0.487 0005 3.77 19.3 10 -#> 16 0002 0.487 0006 5.44 19.7 10 -#> 17 0002 0.487 0007 4.72 19.5 10 -#> 18 0002 0.487 0008 4.10 20.8 10 -#> 19 0002 0.487 0009 3.88 17.8 10 -#> 20 0002 0.487 0010 3.89 18.3 10 +#> 1 0001 0.400 0001 4.61 18.8 10 +#> 2 0001 0.400 0002 3.78 19.4 10 +#> 3 0001 0.400 0003 3.28 20.1 10 +#> 4 0001 0.400 0004 4.84 19.4 10 +#> 5 0001 0.400 0005 4.30 21.6 10 +#> 6 0001 0.400 0006 4.18 19.1 10 +#> 7 0001 0.400 0007 4.06 17.5 10 +#> 8 0001 0.400 0008 4.10 20.8 10 +#> 9 0001 0.400 0009 4.74 18.5 10 +#> 10 0001 0.400 0010 4.30 18.6 10 +#> 11 0002 0.487 0001 3.10 20.6 10 +#> 12 0002 0.487 0002 2.75 20 10 +#> 13 0002 0.487 0003 3.71 19.2 10 +#> 14 0002 0.487 0004 5.34 20.4 10 +#> 15 0002 0.487 0005 3.84 18.5 10 +#> 16 0002 0.487 0006 4.35 19.4 10 +#> 17 0002 0.487 0007 5.48 19.4 10 +#> 18 0002 0.487 0008 3.62 22 10 +#> 19 0002 0.487 0009 4.00 17.7 10 +#> 20 0002 0.487 0010 2.60 19.1 10 df_portf <- sim_test_data_portfolio(df_config) df_portf -#> # A tibble: 3,829 × 8 -#> study_id ae_per_visit_mean site_number max_visit…¹ max_v…² patnum visit n_ae -#> <chr> <dbl> <chr> <dbl> <dbl> <chr> <int> <int> -#> 1 0001 0.401 0001 3.41 20.1 0001 1 0 -#> 2 0001 0.401 0001 3.41 20.1 0001 2 0 -#> 3 0001 0.401 0001 3.41 20.1 0001 3 2 -#> 4 0001 0.401 0001 3.41 20.1 0001 4 2 -#> 5 0001 0.401 0001 3.41 20.1 0001 5 2 -#> 6 0001 0.401 0001 3.41 20.1 0001 6 2 -#> 7 0001 0.401 0001 3.41 20.1 0001 7 2 -#> 8 0001 0.401 0001 3.41 20.1 0001 8 3 -#> 9 0001 0.401 0001 3.41 20.1 0001 9 3 -#> 10 0001 0.401 0001 3.41 20.1 0001 10 3 -#> # … with 3,819 more rows, and abbreviated variable names ¹​max_visit_sd, -#> # ²​max_visit_mean +#> # A tibble: 3,792 × 8 +#> study_id ae_per_visit_mean site_number max_visit_sd max_visit_mean patnum +#> <chr> <dbl> <chr> <dbl> <dbl> <chr> +#> 1 0001 0.400 0001 4.61 18.8 0001 +#> 2 0001 0.400 0001 4.61 18.8 0001 +#> 3 0001 0.400 0001 4.61 18.8 0001 +#> 4 0001 0.400 0001 4.61 18.8 0001 +#> 5 0001 0.400 0001 4.61 18.8 0001 +#> 6 0001 0.400 0001 4.61 18.8 0001 +#> 7 0001 0.400 0001 4.61 18.8 0001 +#> 8 0001 0.400 0001 4.61 18.8 0001 +#> 9 0001 0.400 0001 4.61 18.8 0001 +#> 10 0001 0.400 0001 4.61 18.8 0001 +#> # ℹ 3,782 more rows +#> # ℹ 2 more variables: visit <int>, n_ae <int> df_scen <- sim_ur_scenarios(df_portf, extra_ur_sites = 2, @@ -245,23 +248,22 @@

    Examples

    df_scen #> # A tibble: 140 × 14 -#> study…¹ site_…² n_pat n_pat…³ visit…⁴ mean_…⁵ mean_…⁶ n_pat…⁷ extra…⁸ frac_…⁹ -#> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl> -#> 1 0001 0001 10 10 15 5.8 5.87 78 0 0 -#> 2 0001 0001 10 10 15 2.9 5.87 78 0 0.114 -#> 3 0001 0001 10 10 15 0 5.87 78 0 0.114 -#> 4 0001 0001 10 10 15 2.9 5.53 78 1 0.214 -#> 5 0001 0001 10 10 15 0 5.18 78 1 0.214 -#> 6 0001 0001 10 10 15 2.9 5.25 78 2 0.314 -#> 7 0001 0001 10 10 15 0 4.63 78 2 0.314 -#> 8 0001 0002 10 10 17 7.1 6.46 65 0 0 -#> 9 0001 0002 10 10 17 3.55 6.46 65 0 0.133 -#> 10 0001 0002 10 10 17 0 6.46 65 0 0.133 -#> # … with 130 more rows, 4 more variables: ur_rate <dbl>, prob_low <dbl>, -#> # prob_low_adj <dbl>, prob_low_prob_ur <dbl>, and abbreviated variable names -#> # ¹​study_id, ²​site_number, ³​n_pat_with_med75, ⁴​visit_med75, -#> # ⁵​mean_ae_site_med75, ⁶​mean_ae_study_med75, ⁷​n_pat_with_med75_study, -#> # ⁸​extra_ur_sites, ⁹​frac_pat_with_ur +#> study_id site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75 +#> <chr> <chr> <int> <int> <dbl> <dbl> +#> 1 0001 0001 10 9 14 5.78 +#> 2 0001 0001 10 9 14 2.89 +#> 3 0001 0001 10 9 14 0 +#> 4 0001 0001 10 9 14 2.89 +#> 5 0001 0001 10 9 14 0 +#> 6 0001 0001 10 9 14 2.89 +#> 7 0001 0001 10 9 14 0 +#> 8 0001 0002 10 9 15 5.78 +#> 9 0001 0002 10 9 15 2.89 +#> 10 0001 0002 10 9 15 0 +#> # ℹ 130 more rows +#> # ℹ 8 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>, +#> # extra_ur_sites <dbl>, frac_pat_with_ur <dbl>, ur_rate <dbl>, +#> # prob_low <dbl>, prob_low_adj <dbl>, prob_low_prob_ur <dbl> df_perf <- get_portf_perf(df_scen) @@ -269,17 +271,17 @@

    Examples

    #> # A tibble: 27 × 5 #> fpr thresh extra_ur_sites ur_rate tpr #> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 0.001 0.922 0 0 0.05 -#> 2 0.001 0.922 1 0 0.05 -#> 3 0.001 0.922 2 0 0.05 -#> 4 0.001 0.922 0 0.5 1 -#> 5 0.001 0.922 1 0.5 1 -#> 6 0.001 0.922 2 0.5 1 -#> 7 0.001 0.922 0 1 1 -#> 8 0.001 0.922 1 1 1 -#> 9 0.001 0.922 2 1 1 -#> 10 0.01 0.909 0 0 0.05 -#> # … with 17 more rows +#> 1 0.001 0.925 0 0 0.05 +#> 2 0.001 0.925 1 0 0.05 +#> 3 0.001 0.925 2 0 0.05 +#> 4 0.001 0.925 0 0.5 1 +#> 5 0.001 0.925 1 0.5 1 +#> 6 0.001 0.925 2 0.5 1 +#> 7 0.001 0.925 0 1 1 +#> 8 0.001 0.925 1 1 1 +#> 9 0.001 0.925 2 1 1 +#> 10 0.01 0.920 0 0 0.05 +#> # ℹ 17 more rows # } @@ -295,7 +297,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/get_ecd_values.html b/docs/reference/get_ecd_values.html index 5615a4f..6d11bff 100644 --- a/docs/reference/get_ecd_values.html +++ b/docs/reference/get_ecd_values.html @@ -71,6 +71,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -172,28 +175,26 @@

    Examples

    get_ecd_values(df_sim_studies, df_sim_sites, "prob_low") #> # A tibble: 5 × 11 -#> study_id site_…¹ n_pat n_pat…² visit…³ mean_…⁴ mean_…⁵ n_pat…⁶ pval prob_…⁷ -#> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl> -#> 1 A S0001 20 18 17 5.89 7.78 59 0.00966 0.02 -#> 2 A S0002 20 18 15 5.5 6.85 72 0.0452 0.04 -#> 3 A S0003 20 17 16 8.53 6.70 66 1 1 -#> 4 A S0004 20 20 15 6.4 6.63 70 0.767 0.39 -#> 5 A S0005 20 16 16 8.38 6.76 67 1 1 -#> # … with 1 more variable: prob_low_ecd <dbl>, and abbreviated variable names -#> # ¹​site_number, ²​n_pat_with_med75, ³​visit_med75, ⁴​mean_ae_site_med75, -#> # ⁵​mean_ae_study_med75, ⁶​n_pat_with_med75_study, ⁷​prob_low +#> study_id site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75 +#> <chr> <chr> <int> <int> <dbl> <dbl> +#> 1 A S0001 20 18 17 5.89 +#> 2 A S0002 20 18 15 5.5 +#> 3 A S0003 20 17 16 8.53 +#> 4 A S0004 20 20 15 6.4 +#> 5 A S0005 20 16 16 8.38 +#> # ℹ 5 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>, +#> # pval <dbl>, prob_low <dbl>, prob_low_ecd <dbl> get_ecd_values(df_sim_studies, df_sim_sites, "pval") #> # A tibble: 5 × 11 -#> study_id site_…¹ n_pat n_pat…² visit…³ mean_…⁴ mean_…⁵ n_pat…⁶ pval prob_…⁷ -#> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl> -#> 1 A S0001 20 18 17 5.89 7.78 59 0.00966 0.02 -#> 2 A S0002 20 18 15 5.5 6.85 72 0.0452 0.04 -#> 3 A S0003 20 17 16 8.53 6.70 66 1 1 -#> 4 A S0004 20 20 15 6.4 6.63 70 0.767 0.39 -#> 5 A S0005 20 16 16 8.38 6.76 67 1 1 -#> # … with 1 more variable: pval_ecd <dbl>, and abbreviated variable names -#> # ¹​site_number, ²​n_pat_with_med75, ³​visit_med75, ⁴​mean_ae_site_med75, -#> # ⁵​mean_ae_study_med75, ⁶​n_pat_with_med75_study, ⁷​prob_low +#> study_id site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75 +#> <chr> <chr> <int> <int> <dbl> <dbl> +#> 1 A S0001 20 18 17 5.89 +#> 2 A S0002 20 18 15 5.5 +#> 3 A S0003 20 17 16 8.53 +#> 4 A S0004 20 20 15 6.4 +#> 5 A S0005 20 16 16 8.38 +#> # ℹ 5 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>, +#> # pval <dbl>, prob_low <dbl>, pval_ecd <dbl> @@ -208,7 +209,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/get_pat_pool_config.html b/docs/reference/get_pat_pool_config.html index fa15af2..235dbb9 100644 --- a/docs/reference/get_pat_pool_config.html +++ b/docs/reference/get_pat_pool_config.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -148,14 +151,14 @@

    Examples

    #> # A tibble: 8 × 6 #> study_id site_number visit_med75 n_pat_with_med75 n_pat_study pat_pool #> <chr> <chr> <dbl> <int> <dbl> <list> -#> 1 A S0001 15 19 71 <tibble> -#> 2 A S0002 15 18 72 <tibble> -#> 3 A S0003 16 17 66 <tibble> -#> 4 A S0004 15 20 70 <tibble> -#> 5 A S0005 16 16 67 <tibble> -#> 6 B S0001 15 302 602 <tibble> -#> 7 B S0002 15 301 603 <tibble> -#> 8 B S0003 15 301 603 <tibble> +#> 1 A S0001 15 19 71 <tibble> +#> 2 A S0002 15 18 72 <tibble> +#> 3 A S0003 16 17 66 <tibble> +#> 4 A S0004 15 20 70 <tibble> +#> 5 A S0005 16 16 67 <tibble> +#> 6 B S0001 15 302 602 <tibble> +#> 7 B S0002 15 301 603 <tibble> +#> 8 B S0003 15 301 603 <tibble> @@ -170,7 +173,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/get_portf_perf.html b/docs/reference/get_portf_perf.html index 38f78d3..96ad6b4 100644 --- a/docs/reference/get_portf_perf.html +++ b/docs/reference/get_portf_perf.html @@ -68,6 +68,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -186,20 +189,20 @@

    Examples

    df_portf #> # A tibble: 3,896 × 8 -#> study_id ae_per_visit_mean site_number max_visit…¹ max_v…² patnum visit n_ae -#> <chr> <dbl> <chr> <dbl> <dbl> <chr> <int> <int> -#> 1 0001 0.373 0001 3.79 19.2 0001 1 0 -#> 2 0001 0.373 0001 3.79 19.2 0001 2 1 -#> 3 0001 0.373 0001 3.79 19.2 0001 3 1 -#> 4 0001 0.373 0001 3.79 19.2 0001 4 1 -#> 5 0001 0.373 0001 3.79 19.2 0001 5 3 -#> 6 0001 0.373 0001 3.79 19.2 0001 6 4 -#> 7 0001 0.373 0001 3.79 19.2 0001 7 4 -#> 8 0001 0.373 0001 3.79 19.2 0001 8 4 -#> 9 0001 0.373 0001 3.79 19.2 0001 9 4 -#> 10 0001 0.373 0001 3.79 19.2 0001 10 4 -#> # … with 3,886 more rows, and abbreviated variable names ¹​max_visit_sd, -#> # ²​max_visit_mean +#> study_id ae_per_visit_mean site_number max_visit_sd max_visit_mean patnum +#> <chr> <dbl> <chr> <dbl> <dbl> <chr> +#> 1 0001 0.373 0001 3.79 19.2 0001 +#> 2 0001 0.373 0001 3.79 19.2 0001 +#> 3 0001 0.373 0001 3.79 19.2 0001 +#> 4 0001 0.373 0001 3.79 19.2 0001 +#> 5 0001 0.373 0001 3.79 19.2 0001 +#> 6 0001 0.373 0001 3.79 19.2 0001 +#> 7 0001 0.373 0001 3.79 19.2 0001 +#> 8 0001 0.373 0001 3.79 19.2 0001 +#> 9 0001 0.373 0001 3.79 19.2 0001 +#> 10 0001 0.373 0001 3.79 19.2 0001 +#> # ℹ 3,886 more rows +#> # ℹ 2 more variables: visit <int>, n_ae <int> df_scen <- sim_ur_scenarios(df_portf, extra_ur_sites = 2, @@ -213,23 +216,22 @@

    Examples

    df_scen #> # A tibble: 140 × 14 -#> study…¹ site_…² n_pat n_pat…³ visit…⁴ mean_…⁵ mean_…⁶ n_pat…⁷ extra…⁸ frac_…⁹ -#> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl> -#> 1 0001 0001 10 10 15 5.1 5.06 80 0 0 -#> 2 0001 0001 10 10 15 2.55 5.06 80 0 0.111 -#> 3 0001 0001 10 10 15 0 5.06 80 0 0.111 -#> 4 0001 0001 10 10 15 2.55 4.7 80 1 0.211 -#> 5 0001 0001 10 10 15 0 4.34 80 1 0.211 -#> 6 0001 0001 10 10 15 2.55 4.43 80 2 0.311 -#> 7 0001 0001 10 10 15 0 3.8 80 2 0.311 -#> 8 0001 0002 10 10 16 6.5 5.34 73 0 0 -#> 9 0001 0002 10 10 16 3.25 5.34 73 0 0.120 -#> 10 0001 0002 10 10 16 0 5.34 73 0 0.120 -#> # … with 130 more rows, 4 more variables: ur_rate <dbl>, prob_low <dbl>, -#> # prob_low_adj <dbl>, prob_low_prob_ur <dbl>, and abbreviated variable names -#> # ¹​study_id, ²​site_number, ³​n_pat_with_med75, ⁴​visit_med75, -#> # ⁵​mean_ae_site_med75, ⁶​mean_ae_study_med75, ⁷​n_pat_with_med75_study, -#> # ⁸​extra_ur_sites, ⁹​frac_pat_with_ur +#> study_id site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75 +#> <chr> <chr> <int> <int> <dbl> <dbl> +#> 1 0001 0001 10 10 15 5.1 +#> 2 0001 0001 10 10 15 2.55 +#> 3 0001 0001 10 10 15 0 +#> 4 0001 0001 10 10 15 2.55 +#> 5 0001 0001 10 10 15 0 +#> 6 0001 0001 10 10 15 2.55 +#> 7 0001 0001 10 10 15 0 +#> 8 0001 0002 10 10 16 6.5 +#> 9 0001 0002 10 10 16 3.25 +#> 10 0001 0002 10 10 16 0 +#> # ℹ 130 more rows +#> # ℹ 8 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>, +#> # extra_ur_sites <dbl>, frac_pat_with_ur <dbl>, ur_rate <dbl>, +#> # prob_low <dbl>, prob_low_adj <dbl>, prob_low_prob_ur <dbl> df_perf <- get_portf_perf(df_scen) @@ -247,7 +249,7 @@

    Examples

    #> 8 0.001 0.983 1 1 1 #> 9 0.001 0.983 2 1 1 #> 10 0.01 0.963 0 0 0.05 -#> # … with 17 more rows +#> # ℹ 17 more rows # } @@ -263,7 +265,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/get_site_mean_ae_dev.html b/docs/reference/get_site_mean_ae_dev.html index e148b80..851340d 100644 --- a/docs/reference/get_site_mean_ae_dev.html +++ b/docs/reference/get_site_mean_ae_dev.html @@ -68,6 +68,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -134,7 +137,7 @@

    Value

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/get_visit_med75.html b/docs/reference/get_visit_med75.html index 6106956..1d3968e 100644 --- a/docs/reference/get_visit_med75.html +++ b/docs/reference/get_visit_med75.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -134,7 +137,7 @@

    Value

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/index.html b/docs/reference/index.html index 1206199..b13a79a 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -279,7 +282,7 @@

    Additional Plot Functions -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/is_orivisit.html b/docs/reference/is_orivisit.html index 8d7235d..0aa258d 100644 --- a/docs/reference/is_orivisit.html +++ b/docs/reference/is_orivisit.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -124,7 +127,7 @@

    Value

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/is_simaerep.html b/docs/reference/is_simaerep.html index 115a25a..b8032de 100644 --- a/docs/reference/is_simaerep.html +++ b/docs/reference/is_simaerep.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -124,7 +127,7 @@

    Value

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/orivisit.html b/docs/reference/orivisit.html index 5f3bca2..c0f208c 100644 --- a/docs/reference/orivisit.html +++ b/docs/reference/orivisit.html @@ -68,6 +68,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -154,20 +157,20 @@

    Examples

    #> The deprecated feature was likely used in the simaerep package. #> Please report the issue to the authors. #> # A tibble: 1,970 × 9 -#> patnum site_number is_ur max_visit_mean max_vi…¹ ae_pe…² visit n_ae study…³ -#> <chr> <chr> <lgl> <dbl> <dbl> <dbl> <int> <int> <chr> -#> 1 P000001 S0001 TRUE 20 4 0.2 1 0 A -#> 2 P000001 S0001 TRUE 20 4 0.2 2 0 A -#> 3 P000001 S0001 TRUE 20 4 0.2 3 0 A -#> 4 P000001 S0001 TRUE 20 4 0.2 4 0 A -#> 5 P000001 S0001 TRUE 20 4 0.2 5 0 A -#> 6 P000001 S0001 TRUE 20 4 0.2 6 0 A -#> 7 P000001 S0001 TRUE 20 4 0.2 7 0 A -#> 8 P000001 S0001 TRUE 20 4 0.2 8 0 A -#> 9 P000001 S0001 TRUE 20 4 0.2 9 0 A -#> 10 P000001 S0001 TRUE 20 4 0.2 10 0 A -#> # … with 1,960 more rows, and abbreviated variable names ¹​max_visit_sd, -#> # ²​ae_per_visit_mean, ³​study_id +#> patnum site_number is_ur max_visit_mean max_visit_sd ae_per_visit_mean visit +#> <chr> <chr> <lgl> <dbl> <dbl> <dbl> <int> +#> 1 P000001 S0001 TRUE 20 4 0.2 1 +#> 2 P000001 S0001 TRUE 20 4 0.2 2 +#> 3 P000001 S0001 TRUE 20 4 0.2 3 +#> 4 P000001 S0001 TRUE 20 4 0.2 4 +#> 5 P000001 S0001 TRUE 20 4 0.2 5 +#> 6 P000001 S0001 TRUE 20 4 0.2 6 +#> 7 P000001 S0001 TRUE 20 4 0.2 7 +#> 8 P000001 S0001 TRUE 20 4 0.2 8 +#> 9 P000001 S0001 TRUE 20 4 0.2 9 +#> 10 P000001 S0001 TRUE 20 4 0.2 10 +#> # ℹ 1,960 more rows +#> # ℹ 2 more variables: n_ae <int>, study_id <chr> @@ -183,7 +186,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/pat_aggr.html b/docs/reference/pat_aggr.html index 6edb543..1dfbec3 100644 --- a/docs/reference/pat_aggr.html +++ b/docs/reference/pat_aggr.html @@ -68,6 +68,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -126,7 +129,7 @@

    Value

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/pat_pool.html b/docs/reference/pat_pool.html index ebc03af..6d2aed0 100644 --- a/docs/reference/pat_pool.html +++ b/docs/reference/pat_pool.html @@ -70,6 +70,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -156,7 +159,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/pipe.html b/docs/reference/pipe.html index 79834a7..408d0c1 100644 --- a/docs/reference/pipe.html +++ b/docs/reference/pipe.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -118,7 +121,7 @@

    Value

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/plot.simaerep.html b/docs/reference/plot.simaerep.html index 4ae2d50..86fbb05 100644 --- a/docs/reference/plot.simaerep.html +++ b/docs/reference/plot.simaerep.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -194,7 +197,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/plot_dots.html b/docs/reference/plot_dots.html index 83eb9e6..089bfc1 100644 --- a/docs/reference/plot_dots.html +++ b/docs/reference/plot_dots.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -189,7 +192,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/plot_sim_example.html b/docs/reference/plot_sim_example.html index 10ac0c3..2f541bd 100644 --- a/docs/reference/plot_sim_example.html +++ b/docs/reference/plot_sim_example.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -193,7 +196,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/plot_sim_examples.html b/docs/reference/plot_sim_examples.html index 84e87ae..611a6de 100644 --- a/docs/reference/plot_sim_examples.html +++ b/docs/reference/plot_sim_examples.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -146,7 +149,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/plot_study.html b/docs/reference/plot_study.html index 5b038ec..11ca9b1 100644 --- a/docs/reference/plot_study.html +++ b/docs/reference/plot_study.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -188,7 +191,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/plot_visit_med75.html b/docs/reference/plot_visit_med75.html index 43a9e5d..0bff9c3 100644 --- a/docs/reference/plot_visit_med75.html +++ b/docs/reference/plot_visit_med75.html @@ -68,6 +68,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -173,7 +176,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/poiss_test_site_ae_vs_study_ae.html b/docs/reference/poiss_test_site_ae_vs_study_ae.html index 3da9efd..6bddb30 100644 --- a/docs/reference/poiss_test_site_ae_vs_study_ae.html +++ b/docs/reference/poiss_test_site_ae_vs_study_ae.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -157,7 +160,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/prep_for_sim.html b/docs/reference/prep_for_sim.html index e049270..53ebcd9 100644 --- a/docs/reference/prep_for_sim.html +++ b/docs/reference/prep_for_sim.html @@ -69,6 +69,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -161,7 +164,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/prob_lower_site_ae_vs_study_ae.html b/docs/reference/prob_lower_site_ae_vs_study_ae.html index 0abc53f..d43a211 100644 --- a/docs/reference/prob_lower_site_ae_vs_study_ae.html +++ b/docs/reference/prob_lower_site_ae_vs_study_ae.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -154,7 +157,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/purrr_bar.html b/docs/reference/purrr_bar.html index ee6d273..a775feb 100644 --- a/docs/reference/purrr_bar.html +++ b/docs/reference/purrr_bar.html @@ -69,6 +69,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -300,7 +303,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/sim_after_prep.html b/docs/reference/sim_after_prep.html index 1bcd4de..36cc026 100644 --- a/docs/reference/sim_after_prep.html +++ b/docs/reference/sim_after_prep.html @@ -68,6 +68,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -161,16 +164,15 @@

    Examples

    df_sim #> # A tibble: 5 × 9 -#> study_id site_number n_pat n_pat_wit…¹ visit…² mean_…³ mean_…⁴ n_pat…⁵ prob_…⁶ -#> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <int> <dbl> -#> 1 A S0001 20 20 15 6.1 6.96 72 0.118 -#> 2 A S0002 20 17 16 6.53 7.43 67 0.132 -#> 3 A S0003 20 17 16 8.35 6.97 67 1 -#> 4 A S0004 20 20 15 6.55 6.83 72 0.353 -#> 5 A S0005 20 16 15 7.62 6.59 76 1 -#> # … with abbreviated variable names ¹​n_pat_with_med75, ²​visit_med75, -#> # ³​mean_ae_site_med75, ⁴​mean_ae_study_med75, ⁵​n_pat_with_med75_study, -#> # ⁶​prob_low +#> study_id site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75 +#> <chr> <chr> <int> <int> <dbl> <dbl> +#> 1 A S0001 20 20 15 6.1 +#> 2 A S0002 20 17 16 6.53 +#> 3 A S0003 20 17 16 8.35 +#> 4 A S0004 20 20 15 6.55 +#> 5 A S0005 20 16 15 7.62 +#> # ℹ 3 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>, +#> # prob_low <dbl> @@ -185,7 +187,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/sim_scenario.html b/docs/reference/sim_scenario.html index 8826f2e..3c9c940 100644 --- a/docs/reference/sim_scenario.html +++ b/docs/reference/sim_scenario.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -182,7 +185,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/sim_sites.html b/docs/reference/sim_sites.html index 1789f96..3e49bf8 100644 --- a/docs/reference/sim_sites.html +++ b/docs/reference/sim_sites.html @@ -69,6 +69,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -230,7 +233,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/sim_studies.html b/docs/reference/sim_studies.html index dea6693..6da3ce6 100644 --- a/docs/reference/sim_studies.html +++ b/docs/reference/sim_studies.html @@ -69,6 +69,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -201,21 +204,20 @@

    Examples

    sim_studies(df_visit, df_site, r = 3, keep_ae = TRUE) #> # A tibble: 24 × 10 -#> r study_id site_n…¹ visit…² n_pat…³ n_pat…⁴ n_ae_…⁵ n_ae_…⁶ pval prob_…⁷ -#> <dbl> <chr> <chr> <dbl> <int> <dbl> <chr> <chr> <dbl> <dbl> -#> 1 1 A S0001 16 20 68 7,13,7… 5,4,1,… 1 1 -#> 2 1 A S0002 18 17 57 1,10,4… 6,16,7… 1 1 -#> 3 1 A S0003 17 18 63 13,11,… 10,6,4… 1 1 -#> 4 1 A S0004 14 18 76 3,9,9,… 5,4,7,… 0.201 0.179 -#> 5 1 A S0005 15 18 72 9,4,7,… 9,13,4… 1 1 -#> 6 1 B S0001 15 297 597 9,8,5,… 6,11,4… 1 1 -#> 7 1 B S0002 15 296 598 14,8,7… 5,6,9,… 1 1 -#> 8 1 B S0003 15 301 593 9,5,8,… 9,7,7,… 1 0.526 -#> 9 2 A S0001 16 20 68 10,7,5… 2,6,3,… 1 1 -#> 10 2 A S0002 18 17 57 12,12,… 5,5,13… 1 1 -#> # … with 14 more rows, and abbreviated variable names ¹​site_number, -#> # ²​visit_med75, ³​n_pat_with_med75, ⁴​n_pat_study, ⁵​n_ae_site, ⁶​n_ae_study, -#> # ⁷​prob_low +#> r study_id site_number visit_med75 n_pat_with_med75 n_pat_study n_ae_site +#> <dbl> <chr> <chr> <dbl> <int> <dbl> <chr> +#> 1 1 A S0001 16 20 68 7,13,7,4… +#> 2 1 A S0002 18 17 57 1,10,4,4… +#> 3 1 A S0003 17 18 63 13,11,13… +#> 4 1 A S0004 14 18 76 3,9,9,9,… +#> 5 1 A S0005 15 18 72 9,4,7,6,… +#> 6 1 B S0001 15 297 597 9,8,5,7,… +#> 7 1 B S0002 15 296 598 14,8,7,1… +#> 8 1 B S0003 15 301 593 9,5,8,6,… +#> 9 2 A S0001 16 20 68 10,7,5,3… +#> 10 2 A S0002 18 17 57 12,12,6,… +#> # ℹ 14 more rows +#> # ℹ 3 more variables: n_ae_study <chr>, pval <dbl>, prob_low <dbl> # } if (FALSE) { # parallel processing ------------------------- @@ -238,7 +240,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/sim_test_data_patient.html b/docs/reference/sim_test_data_patient.html index 2f61929..ce8d012 100644 --- a/docs/reference/sim_test_data_patient.html +++ b/docs/reference/sim_test_data_patient.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -195,7 +198,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/sim_test_data_portfolio.html b/docs/reference/sim_test_data_portfolio.html index ec1978d..f470e8a 100644 --- a/docs/reference/sim_test_data_portfolio.html +++ b/docs/reference/sim_test_data_portfolio.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -211,20 +214,20 @@

    Examples

    df_portf #> # A tibble: 3,729 × 8 -#> study_id ae_per_visit_mean site_number max_visit…¹ max_v…² patnum visit n_ae -#> <chr> <dbl> <chr> <dbl> <dbl> <chr> <int> <int> -#> 1 0001 0.355 0001 4.23 19.9 0001 1 1 -#> 2 0001 0.355 0001 4.23 19.9 0001 2 2 -#> 3 0001 0.355 0001 4.23 19.9 0001 3 2 -#> 4 0001 0.355 0001 4.23 19.9 0001 4 2 -#> 5 0001 0.355 0001 4.23 19.9 0001 5 2 -#> 6 0001 0.355 0001 4.23 19.9 0001 6 2 -#> 7 0001 0.355 0001 4.23 19.9 0001 7 2 -#> 8 0001 0.355 0001 4.23 19.9 0001 8 3 -#> 9 0001 0.355 0001 4.23 19.9 0001 9 3 -#> 10 0001 0.355 0001 4.23 19.9 0001 10 3 -#> # … with 3,719 more rows, and abbreviated variable names ¹​max_visit_sd, -#> # ²​max_visit_mean +#> study_id ae_per_visit_mean site_number max_visit_sd max_visit_mean patnum +#> <chr> <dbl> <chr> <dbl> <dbl> <chr> +#> 1 0001 0.355 0001 4.23 19.9 0001 +#> 2 0001 0.355 0001 4.23 19.9 0001 +#> 3 0001 0.355 0001 4.23 19.9 0001 +#> 4 0001 0.355 0001 4.23 19.9 0001 +#> 5 0001 0.355 0001 4.23 19.9 0001 +#> 6 0001 0.355 0001 4.23 19.9 0001 +#> 7 0001 0.355 0001 4.23 19.9 0001 +#> 8 0001 0.355 0001 4.23 19.9 0001 +#> 9 0001 0.355 0001 4.23 19.9 0001 +#> 10 0001 0.355 0001 4.23 19.9 0001 +#> # ℹ 3,719 more rows +#> # ℹ 2 more variables: visit <int>, n_ae <int> df_scen <- sim_ur_scenarios(df_portf, extra_ur_sites = 2, @@ -238,23 +241,22 @@

    Examples

    df_scen #> # A tibble: 140 × 14 -#> study…¹ site_…² n_pat n_pat…³ visit…⁴ mean_…⁵ mean_…⁶ n_pat…⁷ extra…⁸ frac_…⁹ -#> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl> -#> 1 0001 0001 10 8 21 6.25 7.75 28 0 0 -#> 2 0001 0001 10 8 21 3.12 7.75 28 0 0.222 -#> 3 0001 0001 10 8 21 0 7.75 28 0 0.222 -#> 4 0001 0001 10 8 21 3.12 7.30 28 1 0.333 -#> 5 0001 0001 10 8 21 0 6.86 28 1 0.333 -#> 6 0001 0001 10 8 21 3.12 6.79 28 2 0.444 -#> 7 0001 0001 10 8 21 0 5.82 28 2 0.444 -#> 8 0001 0002 10 10 14 3.8 4.74 74 0 0 -#> 9 0001 0002 10 10 14 1.9 4.74 74 0 0.119 -#> 10 0001 0002 10 10 14 0 4.74 74 0 0.119 -#> # … with 130 more rows, 4 more variables: ur_rate <dbl>, prob_low <dbl>, -#> # prob_low_adj <dbl>, prob_low_prob_ur <dbl>, and abbreviated variable names -#> # ¹​study_id, ²​site_number, ³​n_pat_with_med75, ⁴​visit_med75, -#> # ⁵​mean_ae_site_med75, ⁶​mean_ae_study_med75, ⁷​n_pat_with_med75_study, -#> # ⁸​extra_ur_sites, ⁹​frac_pat_with_ur +#> study_id site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75 +#> <chr> <chr> <int> <int> <dbl> <dbl> +#> 1 0001 0001 10 8 21 6.25 +#> 2 0001 0001 10 8 21 3.12 +#> 3 0001 0001 10 8 21 0 +#> 4 0001 0001 10 8 21 3.12 +#> 5 0001 0001 10 8 21 0 +#> 6 0001 0001 10 8 21 3.12 +#> 7 0001 0001 10 8 21 0 +#> 8 0001 0002 10 10 14 3.8 +#> 9 0001 0002 10 10 14 1.9 +#> 10 0001 0002 10 10 14 0 +#> # ℹ 130 more rows +#> # ℹ 8 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>, +#> # extra_ur_sites <dbl>, frac_pat_with_ur <dbl>, ur_rate <dbl>, +#> # prob_low <dbl>, prob_low_adj <dbl>, prob_low_prob_ur <dbl> df_perf <- get_portf_perf(df_scen) @@ -272,7 +274,7 @@

    Examples

    #> 8 0.001 0.916 1 1 1 #> 9 0.001 0.916 2 1 1 #> 10 0.01 0.913 0 0 0.05 -#> # … with 17 more rows +#> # ℹ 17 more rows # } @@ -288,7 +290,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/sim_test_data_study.html b/docs/reference/sim_test_data_study.html index 89f7d9e..1bd2777 100644 --- a/docs/reference/sim_test_data_study.html +++ b/docs/reference/sim_test_data_study.html @@ -69,6 +69,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -165,43 +168,44 @@

    Examples

    df_visit <- sim_test_data_study(n_pat = 100, n_sites = 5) df_visit[which(df_visit$patnum == "P000001"),] #> # A tibble: 17 × 8 -#> patnum site_number is_ur max_visit_mean max_visit_sd ae_per_vi…¹ visit n_ae -#> <chr> <chr> <lgl> <dbl> <dbl> <dbl> <int> <int> -#> 1 P000001 S0001 FALSE 20 4 0.5 1 0 -#> 2 P000001 S0001 FALSE 20 4 0.5 2 1 -#> 3 P000001 S0001 FALSE 20 4 0.5 3 1 -#> 4 P000001 S0001 FALSE 20 4 0.5 4 2 -#> 5 P000001 S0001 FALSE 20 4 0.5 5 4 -#> 6 P000001 S0001 FALSE 20 4 0.5 6 5 -#> 7 P000001 S0001 FALSE 20 4 0.5 7 6 -#> 8 P000001 S0001 FALSE 20 4 0.5 8 6 -#> 9 P000001 S0001 FALSE 20 4 0.5 9 6 -#> 10 P000001 S0001 FALSE 20 4 0.5 10 6 -#> 11 P000001 S0001 FALSE 20 4 0.5 11 7 -#> 12 P000001 S0001 FALSE 20 4 0.5 12 7 -#> 13 P000001 S0001 FALSE 20 4 0.5 13 8 -#> 14 P000001 S0001 FALSE 20 4 0.5 14 8 -#> 15 P000001 S0001 FALSE 20 4 0.5 15 9 -#> 16 P000001 S0001 FALSE 20 4 0.5 16 12 -#> 17 P000001 S0001 FALSE 20 4 0.5 17 12 -#> # … with abbreviated variable name ¹​ae_per_visit_mean +#> patnum site_number is_ur max_visit_mean max_visit_sd ae_per_visit_mean visit +#> <chr> <chr> <lgl> <dbl> <dbl> <dbl> <int> +#> 1 P000001 S0001 FALSE 20 4 0.5 1 +#> 2 P000001 S0001 FALSE 20 4 0.5 2 +#> 3 P000001 S0001 FALSE 20 4 0.5 3 +#> 4 P000001 S0001 FALSE 20 4 0.5 4 +#> 5 P000001 S0001 FALSE 20 4 0.5 5 +#> 6 P000001 S0001 FALSE 20 4 0.5 6 +#> 7 P000001 S0001 FALSE 20 4 0.5 7 +#> 8 P000001 S0001 FALSE 20 4 0.5 8 +#> 9 P000001 S0001 FALSE 20 4 0.5 9 +#> 10 P000001 S0001 FALSE 20 4 0.5 10 +#> 11 P000001 S0001 FALSE 20 4 0.5 11 +#> 12 P000001 S0001 FALSE 20 4 0.5 12 +#> 13 P000001 S0001 FALSE 20 4 0.5 13 +#> 14 P000001 S0001 FALSE 20 4 0.5 14 +#> 15 P000001 S0001 FALSE 20 4 0.5 15 +#> 16 P000001 S0001 FALSE 20 4 0.5 16 +#> 17 P000001 S0001 FALSE 20 4 0.5 17 +#> # ℹ 1 more variable: n_ae <int> df_visit <- sim_test_data_study(n_pat = 100, n_sites = 5, frac_site_with_ur = 0.2, ur_rate = 0.5) df_visit[which(df_visit$patnum == "P000001"),] #> # A tibble: 23 × 8 -#> patnum site_number is_ur max_visit_mean max_visit_sd ae_per_vi…¹ visit n_ae -#> <chr> <chr> <lgl> <dbl> <dbl> <dbl> <int> <int> -#> 1 P000001 S0001 TRUE 20 4 0.25 1 0 -#> 2 P000001 S0001 TRUE 20 4 0.25 2 0 -#> 3 P000001 S0001 TRUE 20 4 0.25 3 1 -#> 4 P000001 S0001 TRUE 20 4 0.25 4 1 -#> 5 P000001 S0001 TRUE 20 4 0.25 5 1 -#> 6 P000001 S0001 TRUE 20 4 0.25 6 2 -#> 7 P000001 S0001 TRUE 20 4 0.25 7 2 -#> 8 P000001 S0001 TRUE 20 4 0.25 8 2 -#> 9 P000001 S0001 TRUE 20 4 0.25 9 2 -#> 10 P000001 S0001 TRUE 20 4 0.25 10 2 -#> # … with 13 more rows, and abbreviated variable name ¹​ae_per_visit_mean +#> patnum site_number is_ur max_visit_mean max_visit_sd ae_per_visit_mean visit +#> <chr> <chr> <lgl> <dbl> <dbl> <dbl> <int> +#> 1 P000001 S0001 TRUE 20 4 0.25 1 +#> 2 P000001 S0001 TRUE 20 4 0.25 2 +#> 3 P000001 S0001 TRUE 20 4 0.25 3 +#> 4 P000001 S0001 TRUE 20 4 0.25 4 +#> 5 P000001 S0001 TRUE 20 4 0.25 5 +#> 6 P000001 S0001 TRUE 20 4 0.25 6 +#> 7 P000001 S0001 TRUE 20 4 0.25 7 +#> 8 P000001 S0001 TRUE 20 4 0.25 8 +#> 9 P000001 S0001 TRUE 20 4 0.25 9 +#> 10 P000001 S0001 TRUE 20 4 0.25 10 +#> # ℹ 13 more rows +#> # ℹ 1 more variable: n_ae <int> @@ -216,7 +220,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/sim_ur_scenarios.html b/docs/reference/sim_ur_scenarios.html index e5b7a48..5269b91 100644 --- a/docs/reference/sim_ur_scenarios.html +++ b/docs/reference/sim_ur_scenarios.html @@ -68,6 +68,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -297,20 +300,20 @@

    Examples

    df_portf #> # A tibble: 3,815 × 8 -#> study_id ae_per_visit_mean site_number max_visit…¹ max_v…² patnum visit n_ae -#> <chr> <dbl> <chr> <dbl> <dbl> <chr> <int> <int> -#> 1 0001 0.416 0001 3.39 18.2 0001 1 1 -#> 2 0001 0.416 0001 3.39 18.2 0001 2 1 -#> 3 0001 0.416 0001 3.39 18.2 0001 3 1 -#> 4 0001 0.416 0001 3.39 18.2 0001 4 1 -#> 5 0001 0.416 0001 3.39 18.2 0001 5 2 -#> 6 0001 0.416 0001 3.39 18.2 0001 6 3 -#> 7 0001 0.416 0001 3.39 18.2 0001 7 4 -#> 8 0001 0.416 0001 3.39 18.2 0001 8 4 -#> 9 0001 0.416 0001 3.39 18.2 0001 9 4 -#> 10 0001 0.416 0001 3.39 18.2 0001 10 5 -#> # … with 3,805 more rows, and abbreviated variable names ¹​max_visit_sd, -#> # ²​max_visit_mean +#> study_id ae_per_visit_mean site_number max_visit_sd max_visit_mean patnum +#> <chr> <dbl> <chr> <dbl> <dbl> <chr> +#> 1 0001 0.416 0001 3.39 18.2 0001 +#> 2 0001 0.416 0001 3.39 18.2 0001 +#> 3 0001 0.416 0001 3.39 18.2 0001 +#> 4 0001 0.416 0001 3.39 18.2 0001 +#> 5 0001 0.416 0001 3.39 18.2 0001 +#> 6 0001 0.416 0001 3.39 18.2 0001 +#> 7 0001 0.416 0001 3.39 18.2 0001 +#> 8 0001 0.416 0001 3.39 18.2 0001 +#> 9 0001 0.416 0001 3.39 18.2 0001 +#> 10 0001 0.416 0001 3.39 18.2 0001 +#> # ℹ 3,805 more rows +#> # ℹ 2 more variables: visit <int>, n_ae <int> df_scen <- sim_ur_scenarios(df_portf, extra_ur_sites = 2, @@ -324,23 +327,22 @@

    Examples

    df_scen #> # A tibble: 140 × 14 -#> study…¹ site_…² n_pat n_pat…³ visit…⁴ mean_…⁵ mean_…⁶ n_pat…⁷ extra…⁸ frac_…⁹ -#> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl> -#> 1 0001 0001 10 10 12 5.9 4.74 88 0 0 -#> 2 0001 0001 10 10 12 2.95 4.74 88 0 0.102 -#> 3 0001 0001 10 10 12 0 4.74 88 0 0.102 -#> 4 0001 0001 10 10 12 2.95 4.53 88 1 0.202 -#> 5 0001 0001 10 10 12 0 4.33 88 1 0.202 -#> 6 0001 0001 10 10 12 2.95 4.24 88 2 0.302 -#> 7 0001 0001 10 10 12 0 3.75 88 2 0.302 -#> 8 0001 0002 10 9 15 4.78 6.52 79 0 0 -#> 9 0001 0002 10 9 15 2.39 6.52 79 0 0.102 -#> 10 0001 0002 10 9 15 0 6.52 79 0 0.102 -#> # … with 130 more rows, 4 more variables: ur_rate <dbl>, prob_low <dbl>, -#> # prob_low_adj <dbl>, prob_low_prob_ur <dbl>, and abbreviated variable names -#> # ¹​study_id, ²​site_number, ³​n_pat_with_med75, ⁴​visit_med75, -#> # ⁵​mean_ae_site_med75, ⁶​mean_ae_study_med75, ⁷​n_pat_with_med75_study, -#> # ⁸​extra_ur_sites, ⁹​frac_pat_with_ur +#> study_id site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75 +#> <chr> <chr> <int> <int> <dbl> <dbl> +#> 1 0001 0001 10 10 12 5.9 +#> 2 0001 0001 10 10 12 2.95 +#> 3 0001 0001 10 10 12 0 +#> 4 0001 0001 10 10 12 2.95 +#> 5 0001 0001 10 10 12 0 +#> 6 0001 0001 10 10 12 2.95 +#> 7 0001 0001 10 10 12 0 +#> 8 0001 0002 10 9 15 4.78 +#> 9 0001 0002 10 9 15 2.39 +#> 10 0001 0002 10 9 15 0 +#> # ℹ 130 more rows +#> # ℹ 8 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>, +#> # extra_ur_sites <dbl>, frac_pat_with_ur <dbl>, ur_rate <dbl>, +#> # prob_low <dbl>, prob_low_adj <dbl>, prob_low_prob_ur <dbl> df_perf <- get_portf_perf(df_scen) @@ -358,7 +360,7 @@

    Examples

    #> 8 0.001 0.990 1 1 1 #> 9 0.001 0.990 2 1 1 #> 10 0.01 0.961 0 0 0.05 -#> # … with 17 more rows +#> # ℹ 17 more rows # } @@ -374,7 +376,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/simaerep.html b/docs/reference/simaerep.html index 0cd4853..5a2c252 100644 --- a/docs/reference/simaerep.html +++ b/docs/reference/simaerep.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -251,7 +254,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/site_aggr.html b/docs/reference/site_aggr.html index 160abaf..57e3502 100644 --- a/docs/reference/site_aggr.html +++ b/docs/reference/site_aggr.html @@ -67,6 +67,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -196,7 +199,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/with_progress_cnd.html b/docs/reference/with_progress_cnd.html index bcc9104..39e9991 100644 --- a/docs/reference/with_progress_cnd.html +++ b/docs/reference/with_progress_cnd.html @@ -69,6 +69,9 @@
  • Aggregate AEs by Days or Visit?
  • +
  • + Comparing {simaerep} and {gsm} Performance +
  • Changelog @@ -183,7 +186,7 @@

    Examples

    -

    Site built with pkgdown 2.0.5.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/sitemap.xml b/docs/sitemap.xml index 92594ef..d720ff5 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -15,6 +15,9 @@ https://openpharma.github.io/simaerep/articles/do_not_commit.html + + https://openpharma.github.io/simaerep/articles/gsm_perf.html + https://openpharma.github.io/simaerep/articles/index.html diff --git a/vignettes/check_poisson.Rmd b/vignettes/check_poisson.Rmd index 4993ea5..ea55847 100644 --- a/vignettes/check_poisson.Rmd +++ b/vignettes/check_poisson.Rmd @@ -95,7 +95,7 @@ df_sim_sites set.seed(1) -future::plan(multiprocess) +plan(multisession, workers = 6) df_sim_studies <- sim_studies(df_visit, df_site, r = 100, @@ -155,3 +155,7 @@ Here we see that the probabilities from the site simulations (x-axis) perfectly ## Conclusion We see that the bootstrap method gives us more accurate probabilities than the p-values derived from poisson.test even though the AE generation in the sample data follows a strict poisson process. For real clinical trial data with different types of studies for which it is not clear whether AE generation is truly based on a pure poisson process we recommend to use the bootstrap method. If poisson.test shall be used we recommend checking the applicability as we just demonstrated on clinical data set. + +```{r close} +plan(sequential) +``` diff --git a/vignettes/gsm_perf.Rmd b/vignettes/gsm_perf.Rmd new file mode 100644 index 0000000..da781e0 --- /dev/null +++ b/vignettes/gsm_perf.Rmd @@ -0,0 +1,392 @@ +--- +title: "Comparing {simaerep} and {gsm} Performance" +output: + html_document: + toc: true + toc_depth: 3 + toc_float: true + number_sections: true + code_folding: show + collapse: false +editor_options: + chunk_output_type: console +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE, cache = FALSE, message = FALSE) +``` + +# Load +```{r load} +suppressPackageStartupMessages(library(tidyverse)) +suppressPackageStartupMessages(library(knitr)) +suppressPackageStartupMessages(library(furrr)) +suppressPackageStartupMessages(library(future)) +suppressPackageStartupMessages(library(simaerep)) +suppressPackageStartupMessages(library(ggExtra)) +suppressPackageStartupMessages(library(gridExtra)) + +plan(multisession, workers = 6) +``` + +# Install {gsm} + +```{r eval = FALSE} +devtools::install_github("Gilead-BioStats/gsm", ref = "main") +``` + + +# Introduction + +The [{gsm}](https://github.com/Gilead-BioStats/gsm/) R package provides a standardized Risk Based Quality Monitoring (RBQM) framework for clinical trials that pairs a flexible data pipeline with robust reports. It also uses Funnel Plots to flag outliers which provide broader tolerance limits for sites with low exposure and narrower limits for sites with higher exposure. This method is different to the event rate based limits we have used in previous heuristics to measure {simaerep} performance. Funnel plots are discussed in greater detail by [Zink et al. 2018](https://doi.org/10.1177/2168479017738981) + +# Prepare Data + +## Load Portfolio Configurations +```{r real_config} + +df_config <- readr::read_csv("ae_profile.csv") + +df_config %>% + head(25) %>% + knitr::kable() +``` + +## Simulate Portfolio + +And again we simulate artificial visit level data. Using parallel processing. At this stage we have simulated compliant test data of a realistic study portfolio. + +```{r sim_portf} + +df_portf <- sim_test_data_portfolio(df_config, parallel = TRUE, progress = TRUE) + +df_portf %>% + head(25) %>% + knitr::kable() +``` + + +# Apply {gsm} + +## Example + +```{r} +dfSUBJ <- df_portf %>% + select(study_id, siteid = site_number, subjid = patnum, timeonstudy = visit) %>% + summarise(timeonstudy = max(timeonstudy), .by = c(study_id, siteid, subjid)) %>% + group_by(study_id) %>% + nest() + +dfAE <- df_portf %>% + select(study_id, subjid = patnum, n_ae) %>% + summarise(n_ae = max(n_ae), .by = c(study_id, subjid)) %>% + filter(n_ae > 0) %>% + mutate(n_ae = map(n_ae, ~ tibble(n = seq(1, .)), .progress = TRUE)) %>% + unnest(n_ae) %>% + select(- n) %>% + group_by(study_id) %>% + nest() +``` + +```{r} +dfInput <- gsm::AE_Map_Raw(list(dfSUBJ = dfSUBJ$data[[1]], dfAE = dfAE$data[[1]])) +dfInput + +dfTransformed <- gsm::Transform_Rate( + dfInput, + strNumeratorCol = "Count", + strDenominatorCol = "Exposure" + ) +dfTransformed + +dfAnalyzed <- gsm::Analyze_NormalApprox(dfTransformed) +dfAnalyzed + +dfFlagged <- gsm::Flag_NormalApprox(dfAnalyzed, vThreshold = c(-3, -2, 2, 3)) +dfFlagged + +dfSummary <- gsm::Summarize(dfFlagged) +dfSummary + +dfBounds <- gsm::Analyze_NormalApprox_PredictBounds(dfTransformed, vThreshold = c(-3, -2, 2, 3)) +dfBounds + +chart <- gsm::Visualize_Scatter(dfFlagged, dfBounds) +chart +``` + +## Simulate UR + +```{r} +sim_gsm_ur <- function(site, ur_rate, dfTransformed) { + dfTransformed <- dfTransformed %>% + mutate( + Numerator = ifelse(GroupID == site, Numerator * (1 - ur_rate), Numerator), + Metric = Numerator / Denominator + ) + + gsm::Analyze_NormalApprox(dfTransformed) %>% + filter(GroupID == site) %>% + pull(Score) +} + +sim_gsm_ur("0001", ur_rate = 0.75, dfTransformed) +``` + + +```{r} +df_sim <- dfSUBJ %>% + inner_join(dfAE, by = "study_id") %>% + ungroup() %>% + mutate( + trans = map2(data.x, data.y, ~ gsm::AE_Map_Raw(list(dfSUBJ = .x, dfAE = .y))), + trans = map(trans, ~ gsm::Transform_Rate(., strNumeratorCol = "Count", strDenominatorCol = "Exposure")), + sites = map(data.x, ~ distinct(., siteid)) + ) %>% + select(- starts_with("data.")) %>% + unnest(sites) %>% + mutate(ur = list(tibble(ur_rate = c(0, 0.1, 0.25, 0.5, 0.75, 1)))) %>% + unnest(ur) %>% + mutate( + score = pmap_dbl(list(siteid, ur_rate, trans), sim_gsm_ur, .progress = TRUE) + ) + + +df_sim +``` + +# UR {simaerep} + +```{r sim_ur_scen_kable} +df_sim_simaerep <- sim_ur_scenarios( + df_portf, + extra_ur_sites = 0, + ur_rate = c(0, 0.1, 0.25, 0.5, 0.75, 1), + parallel = TRUE, + poisson = TRUE, + prob_lower = TRUE, + progress = TRUE +) +``` + +# Evaluate + +## Combine Results + +```{r} + +df_sim_thresh2 <- df_sim %>% + mutate( + is_ur = score <= -2, + type = "{gsm} - thresh: -2", + site_number = siteid + ) %>% + select(type, study_id, site_number, ur_rate, is_ur, score) + + +df_sim_simaerep_threshp95 <- df_sim_simaerep %>% + mutate( + is_ur = prob_low_prob_ur >= 0.95, + type = "{simaerep} - thresh: 0.95" + ) %>% + select(type, study_id, site_number, ur_rate, is_ur, score = prob_low_prob_ur) + + +df_eval <- bind_rows( + df_sim_thresh2, + df_sim_simaerep_threshp95, +) +``` + + +## Aggregate + +```{r} +get_prop_test_ci95 <- function(..., ix) { + + stopifnot(ix %in% c(1, 2)) + + tryCatch( + prop.test(...)$conf.int[ix], + error = function(cnd) c(NA, NA)[ix] + ) +} + +aggr_results <- function(df_eval) { + +df_perf <- df_eval %>% + summarise( + n = n(), + .by = c(type, ur_rate, is_ur) + ) %>% + pivot_wider( + names_from = is_ur, + values_from = n, + names_prefix = "is_ur_", + values_fill = 0 + ) %>% + mutate( + n_sites = is_ur_TRUE + is_ur_FALSE + is_ur_NA, + ratio = is_ur_TRUE / n_sites, + ratio_type = ifelse(ur_rate == 0, "fpr", "tpr"), + ci95_low = map2_dbl(is_ur_TRUE, n_sites, ~ get_prop_test_ci95(.x, .y, ix = 1)), + ci95_high = map2_dbl(is_ur_TRUE, n_sites, ~ get_prop_test_ci95(.x, .y, ix = 2)) + ) +} + +df_perf <- aggr_results(df_eval) +``` + +## Table + +```{r} +df_perf %>% + knitr::kable(digits = 3) + +``` + + + +## Plot Performance Metrics + +- {gsm} has better performance than {simaerep} + +```{r, plot, fig.width=6, fig.height = 10} + +plot_perf <- function(df_perf) { + + df_perf %>% + mutate(ur_rate = paste0("under-reporting rate: ", ur_rate, " - ", ratio_type), + ur_rate = ifelse(str_detect(ur_rate, "fpr"), "fpr", ur_rate)) %>% + group_by(ur_rate) %>% + ggplot(aes(type, ratio)) + + geom_errorbar(aes(ymin = ci95_low, ymax = ci95_high), color = "violetred2") + + facet_wrap(~ ur_rate, ncol = 1) + + coord_flip() + + labs( + x = "", + y = "CI95 Performance Ratio", + title = "{simaerep} vs {gsm} Performance", + subtitle = "Only one under-reporting site per study." + ) +} + +plot_perf(df_perf) +``` + + +## Analysis + +- {gsm} correctly flags more sites in studies with <= 0.25 AE per Visit with higher number of patients per site +- {simarep} correctly flags more sites in studies with 0.25 - 0.5 AE per visit with smaller number of patients per site +- false positive flagging for {simaerep} is similarly distributed as true positive flagging +- false positive flagging for {gsm} evenly covers the whole distribution range + +```{r} +df_qu <- df_portf %>% + summarize(n_pat = n_distinct(patnum), .by = c("study_id", "site_number", "ae_per_visit_mean")) %>% + left_join( + df_sim %>% + select(study_id, site_number = siteid, ur_rate, score) + ) %>% + left_join( + df_sim_simaerep %>% + select(study_id, site_number, ur_rate, prob_low_prob_ur) + ) %>% + mutate( + flag = case_when( + score <= -2 & prob_low_prob_ur >= 0.95 ~ "double", + score <= -2 ~ "gsm only", + prob_low_prob_ur >= 0.95 ~ "simaerep only", + TRUE ~ "miss"), + flag = fct_relevel(flag,"simaerep only", "gsm only", "double", "miss") + ) + +plot_flags <- function(df, ur_rate) { + + p <- df %>% + filter(ur_rate == ur_rate) %>% + ggplot(aes(log(n_pat), ae_per_visit_mean, colour = flag)) + + geom_point(alpha = 0.25) + + theme(legend.position = "bottom", aspect.ratio = 1) + + labs(x = "Log Number of Patiends per Site", y = "AE per Visit Study Average", title = paste("Under Rporting Rate:", ur_rate)) + + ggExtra::ggMarginal(p, groupColour = TRUE, groupFill = TRUE) + +} + +p_p5 <- plot_flags(df_qu, 0.5) + +p_p75 <- plot_flags(df_qu, 0.75) +p_1 <- plot_flags(df_qu, 1) + +p_fpr <- df_qu %>% + filter(ur_rate == 0, flag != "miss") %>% + ggplot(aes(log(n_pat), ae_per_visit_mean, colour = flag)) + + geom_point(alpha = 0.25) + + theme(legend.position = "bottom", aspect.ratio = 1) + + labs(x = "Log Number of Patiends per Site", y = "AE per Visit Study Average", title = paste("Fals Positive Flags")) + +p_fpr <- ggExtra::ggMarginal(p_fpr, groupColour = TRUE, groupFill = TRUE) + +gridExtra::grid.arrange(p_p5, p_p75, p_1, p_fpr, ncol = 2) +``` + +## Combine both approaches + +- clear performance increase only for studies with > 1 ae per visit rate, which is very rare. + +```{r} + +cut_off <- 1 + +df_comb1 <- df_qu %>% + mutate( + type = "comb. ae/visit thresh: 1", + is_ur = (ae_per_visit_mean <= 1 & score <= -2) | ae_per_visit_mean > 1 & prob_low_prob_ur >= 0.95 + ) %>% + select(type, study_id, site_number, ur_rate, is_ur) + +df_combp25 <- df_qu %>% + mutate( + type = "comb. ae/visit thresh: 0.25", + is_ur = (ae_per_visit_mean <= 0.25 & score <= -2) | ae_per_visit_mean > 0.25 & prob_low_prob_ur >= 0.95 + ) %>% + select(type, study_id, site_number, ur_rate, is_ur) + +df_combp5 <- df_qu %>% + mutate( + type = "comb. ae/visit thresh: 0.5", + is_ur = (ae_per_visit_mean <= 0.5 & score <= -2) | ae_per_visit_mean > 0.5 & prob_low_prob_ur >= 0.95 + ) %>% + select(type, study_id, site_number, ur_rate, is_ur) + + +df_comb <- bind_rows( + df_eval, + df_comb1, + df_combp25, + df_combp5 +) + +df_comb_perf <- aggr_results(df_comb) + +plot_perf(df_comb_perf) + + labs(title = "Combined Performance with Differect ") +``` + + +# Conclusion + +- Across the portfolio {gsm} flagging performs better than {simaerep} flagging. +- A robust population of studies for which {simaerep} flagging is preferable cannot be identified. + +The simulation assumes a constant AE rate during the the study. This is not true specifically during follow-up periods. By design {simaerep} should be able to handle those cases better than {gsm}. A more complex simulation is needed in order to proof this hypothesis. + +[Zink et al. 2018](https://doi.org/10.1177/2168479017738981) addressees the limitation of funnel plots for changing event rates in different phases of the study and suggests to stratify the data by relative time windows and present the results as a heatmap. + + + +```{r close} +plan(sequential) +``` From 574f4f15bb2eadc8a2a31da325cd20cd4e41f285 Mon Sep 17 00:00:00 2001 From: Bjoern Koneswarakantha Date: Wed, 7 Feb 2024 12:31:12 +0100 Subject: [PATCH 2/7] fix format gsm_perf --- docs/articles/gsm_perf.html | 343 +++++++++--------- .../gsm_perf_files/figure-html/plot-1.png | Bin 114140 -> 115010 bytes .../figure-html/unnamed-chunk-10-1.png | Bin 195193 -> 203684 bytes .../figure-html/unnamed-chunk-3-1.png | Bin 106592 -> 106488 bytes .../figure-html/unnamed-chunk-9-1.png | Bin 212582 -> 787539 bytes vignettes/gsm_perf.Rmd | 22 +- 6 files changed, 182 insertions(+), 183 deletions(-) diff --git a/docs/articles/gsm_perf.html b/docs/articles/gsm_perf.html index 2c5415f..df01c6a 100644 --- a/docs/articles/gsm_perf.html +++ b/docs/articles/gsm_perf.html @@ -543,7 +543,7 @@

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    ExampledfInput <- gsm::AE_Map_Raw(list(dfSUBJ = dfSUBJ$data[[1]], dfAE = dfAE$data[[1]])) dfInput
    ## # A tibble: 113 × 5
    -##    SubjectID SiteID Exposure Count  Rate
    -##    <chr>     <chr>     <int> <int> <dbl>
    -##  1 0001      0001         22    11 0.5  
    -##  2 0002      0001         21     7 0.333
    -##  3 0003      0002         27     8 0.296
    -##  4 0004      0002         32     4 0.125
    -##  5 0005      0003         19     5 0.263
    -##  6 0006      0003         18     3 0.167
    -##  7 0007      0003         19     4 0.211
    -##  8 0008      0003         19     6 0.316
    -##  9 0009      0004         32    11 0.344
    -## 10 0010      0004         22     8 0.364
    +##    SubjectID SiteID Exposure Count   Rate
    +##    <chr>     <chr>     <int> <int>  <dbl>
    +##  1 0001      0001         22     5 0.227 
    +##  2 0002      0001         22     5 0.227 
    +##  3 0003      0002         30     9 0.3   
    +##  4 0004      0002         28     8 0.286 
    +##  5 0005      0003         17     5 0.294 
    +##  6 0006      0003         19     5 0.263 
    +##  7 0007      0003         19     1 0.0526
    +##  8 0008      0003         19     3 0.158 
    +##  9 0009      0004         31     4 0.129 
    +## 10 0010      0004         30     1 0.0333
     ## # ℹ 103 more rows
     dfTransformed <- gsm::Transform_Rate(
    @@ -897,16 +897,16 @@ 

    Example## # A tibble: 44 × 4 ## GroupID Numerator Denominator Metric ## <chr> <int> <int> <dbl> -## 1 0001 18 43 0.419 -## 2 0002 12 59 0.203 -## 3 0003 18 75 0.24 -## 4 0004 61 173 0.353 -## 5 0005 8 30 0.267 -## 6 0006 9 35 0.257 -## 7 0007 8 36 0.222 -## 8 0008 48 182 0.264 -## 9 0009 10 40 0.25 -## 10 0010 39 137 0.285 +## 1 0001 10 44 0.227 +## 2 0002 17 58 0.293 +## 3 0003 14 74 0.189 +## 4 0004 39 173 0.225 +## 5 0005 14 30 0.467 +## 6 0006 16 35 0.457 +## 7 0007 10 36 0.278 +## 8 0008 41 180 0.228 +## 9 0009 12 40 0.3 +## 10 0010 41 132 0.311 ## # ℹ 34 more rows

     dfAnalyzed <- gsm::Analyze_NormalApprox(dfTransformed)
    @@ -914,16 +914,16 @@ 

    Example## # A tibble: 44 × 7 ## GroupID Numerator Denominator Metric OverallMetric Factor Score ## <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> -## 1 0019 23 136 0.169 0.262 1.21 -2.24 -## 2 0016 2 25 0.08 0.262 1.21 -1.88 -## 3 0042 6 43 0.140 0.262 1.21 -1.66 -## 4 0032 3 26 0.115 0.262 1.21 -1.54 -## 5 0011 16 85 0.188 0.262 1.21 -1.40 -## 6 0028 10 55 0.182 0.262 1.21 -1.23 -## 7 0020 12 61 0.197 0.262 1.21 -1.05 -## 8 0002 12 59 0.203 0.262 1.21 -0.929 -## 9 0039 8 41 0.195 0.262 1.21 -0.884 -## 10 0018 28 124 0.226 0.262 1.21 -0.831 +## 1 0003 14 74 0.189 0.270 1.49 -1.28 +## 2 0039 7 42 0.167 0.270 1.49 -1.24 +## 3 0019 44 197 0.223 0.270 1.49 -1.21 +## 4 0004 39 173 0.225 0.270 1.49 -1.08 +## 5 0026 8 44 0.182 0.270 1.49 -1.08 +## 6 0008 41 180 0.228 0.270 1.49 -1.05 +## 7 0021 19 88 0.216 0.270 1.49 -0.936 +## 8 0017 14 67 0.209 0.270 1.49 -0.922 +## 9 0023 4 23 0.174 0.270 1.49 -0.850 +## 10 0027 4 21 0.190 0.270 1.49 -0.672 ## # ℹ 34 more rows

     dfFlagged <- gsm::Flag_NormalApprox(dfAnalyzed, vThreshold = c(-3, -2, 2, 3))
    @@ -931,51 +931,51 @@ 

    Example## # A tibble: 44 × 8 ## GroupID Numerator Denominator Metric OverallMetric Factor Score Flag ## <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> -## 1 0001 18 43 0.419 0.262 1.21 2.12 1 -## 2 0004 61 173 0.353 0.262 1.21 2.46 1 -## 3 0019 23 136 0.169 0.262 1.21 -2.24 -1 -## 4 0016 2 25 0.08 0.262 1.21 -1.88 0 -## 5 0042 6 43 0.140 0.262 1.21 -1.66 0 -## 6 0032 3 26 0.115 0.262 1.21 -1.54 0 -## 7 0011 16 85 0.188 0.262 1.21 -1.40 0 -## 8 0028 10 55 0.182 0.262 1.21 -1.23 0 -## 9 0020 12 61 0.197 0.262 1.21 -1.05 0 -## 10 0002 12 59 0.203 0.262 1.21 -0.929 0 +## 1 0006 16 35 0.457 0.270 1.49 2.04 1 +## 2 0032 14 26 0.538 0.270 1.49 2.53 1 +## 3 0003 14 74 0.189 0.270 1.49 -1.28 0 +## 4 0039 7 42 0.167 0.270 1.49 -1.24 0 +## 5 0019 44 197 0.223 0.270 1.49 -1.21 0 +## 6 0004 39 173 0.225 0.270 1.49 -1.08 0 +## 7 0026 8 44 0.182 0.270 1.49 -1.08 0 +## 8 0008 41 180 0.228 0.270 1.49 -1.05 0 +## 9 0021 19 88 0.216 0.270 1.49 -0.936 0 +## 10 0017 14 67 0.209 0.270 1.49 -0.922 0 ## # ℹ 34 more rows

     dfSummary <- gsm::Summarize(dfFlagged)
     dfSummary
    ## # A tibble: 44 × 6
    -##    GroupID Numerator Denominator Metric  Score  Flag
    -##    <chr>       <int>       <int>  <dbl>  <dbl> <dbl>
    -##  1 0001           18          43  0.419  2.12      1
    -##  2 0004           61         173  0.353  2.46      1
    -##  3 0019           23         136  0.169 -2.24     -1
    -##  4 0031            7          18  0.389  1.11      0
    -##  5 0023            8          23  0.348  0.850     0
    -##  6 0014            6          18  0.333  0.625     0
    -##  7 0036           67         205  0.327  1.92      0
    -##  8 0041           19          59  0.322  0.952     0
    -##  9 0013            8          25  0.32   0.599     0
    -## 10 0021           24          79  0.304  0.767     0
    +##    GroupID Numerator Denominator Metric Score  Flag
    +##    <chr>       <int>       <int>  <dbl> <dbl> <dbl>
    +##  1 0032           14          26  0.538 2.53      1
    +##  2 0006           16          35  0.457 2.04      1
    +##  3 0044           10          20  0.5   1.90      0
    +##  4 0030           12          25  0.48  1.94      0
    +##  5 0005           14          30  0.467 1.99      0
    +##  6 0013           10          25  0.4   1.20      0
    +##  7 0014            7          18  0.389 0.931     0
    +##  8 0037           11          30  0.367 0.977     0
    +##  9 0040           14          39  0.359 1.03      0
    +## 10 0042           17          50  0.34  0.914     0
     ## # ℹ 34 more rows
     dfBounds <- gsm::Analyze_NormalApprox_PredictBounds(dfTransformed, vThreshold = c(-3, -2, 2, 3))
     dfBounds
    -
    ## # A tibble: 1,246 × 5
    +
    ## # A tibble: 1,238 × 5
     ##    Threshold Denominator LogDenominator Numerator  Metric
     ##        <dbl>       <dbl>          <dbl>     <dbl>   <dbl>
    -##  1        -3        31.5           3.45    0.0906 0.00288
    -##  2        -3        32.2           3.47    0.190  0.00591
    -##  3        -3        33.0           3.50    0.291  0.00883
    -##  4        -3        33.7           3.52    0.393  0.0117 
    -##  5        -3        34.5           3.54    0.496  0.0144 
    -##  6        -3        35.2           3.56    0.599  0.0170 
    -##  7        -3        36.0           3.58    0.704  0.0196 
    -##  8        -3        36.7           3.60    0.810  0.0221 
    -##  9        -3        37.4           3.62    0.917  0.0245 
    -## 10        -3        38.2           3.64    1.02   0.0268 
    -## # ℹ 1,236 more rows
    +## 1 -3 36.6 3.60 0.0492 0.00134 +## 2 -3 37.3 3.62 0.147 0.00393 +## 3 -3 38.0 3.64 0.245 0.00645 +## 4 -3 38.8 3.66 0.345 0.00889 +## 5 -3 39.5 3.68 0.445 0.0113 +## 6 -3 40.2 3.69 0.546 0.0136 +## 7 -3 40.9 3.71 0.648 0.0158 +## 8 -3 41.6 3.73 0.751 0.0180 +## 9 -3 42.3 3.75 0.854 0.0202 +## 10 -3 43.1 3.76 0.959 0.0223 +## # ℹ 1,228 more rows
     chart <- gsm::Visualize_Scatter(dfFlagged, dfBounds)
     chart
    @@ -998,7 +998,7 @@

    Simulate UR} sim_gsm_ur("0001", ur_rate = 0.75, dfTransformed)

    -
    ## [1] -2.071082
    +
    ## [1] -2.406863
     df_sim <- dfSUBJ %>%
       inner_join(dfAE, by = "study_id") %>%
    @@ -1015,7 +1015,7 @@ 

    Simulate URmutate( score = pmap_dbl(list(siteid, ur_rate, trans), sim_gsm_ur, .progress = TRUE) )

    -
    ## Warning: There were 529 warnings in `mutate()`.
    +
    ## Warning: There were 530 warnings in `mutate()`.
     ## The first warning was:
     ##  In argument: `z_i = ifelse(...)`.
     ## Caused by warning:
    @@ -1023,22 +1023,22 @@ 

    Simulate UR## In argument: `z_0 = ifelse(...)`. ## Caused by warning in `sqrt()`: ## ! NaNs produced -## Run `dplyr::last_dplyr_warnings()` to see the 528 remaining warnings.

    +## Run `dplyr::last_dplyr_warnings()` to see the 529 remaining warnings.
     df_sim
    ## # A tibble: 93,942 × 5
    -##    study_id trans             siteid ur_rate  score
    -##    <chr>    <list>            <chr>    <dbl>  <dbl>
    -##  1 0001     <tibble [44 × 4]> 0001      0     2.12 
    -##  2 0001     <tibble [44 × 4]> 0001      0.1   1.60 
    -##  3 0001     <tibble [44 × 4]> 0001      0.25  0.760
    -##  4 0001     <tibble [44 × 4]> 0001      0.5  -0.704
    -##  5 0001     <tibble [44 × 4]> 0001      0.75 -2.07 
    -##  6 0001     <tibble [44 × 4]> 0001      1    -3.20 
    -##  7 0001     <tibble [44 × 4]> 0002      0    -0.929
    -##  8 0001     <tibble [44 × 4]> 0002      0.1  -1.23 
    -##  9 0001     <tibble [44 × 4]> 0002      0.25 -1.68 
    -## 10 0001     <tibble [44 × 4]> 0002      0.5  -2.36 
    +##    study_id trans             siteid ur_rate   score
    +##    <chr>    <list>            <chr>    <dbl>   <dbl>
    +##  1 0001     <tibble [44 × 4]> 0001      0    -0.523 
    +##  2 0001     <tibble [44 × 4]> 0001      0.1  -0.794 
    +##  3 0001     <tibble [44 × 4]> 0001      0.25 -1.19  
    +##  4 0001     <tibble [44 × 4]> 0001      0.5  -1.82  
    +##  5 0001     <tibble [44 × 4]> 0001      0.75 -2.41  
    +##  6 0001     <tibble [44 × 4]> 0001      1    -2.93  
    +##  7 0001     <tibble [44 × 4]> 0002      0     0.325 
    +##  8 0001     <tibble [44 × 4]> 0002      0.1  -0.0789
    +##  9 0001     <tibble [44 × 4]> 0002      0.25 -0.681 
    +## 10 0001     <tibble [44 × 4]> 0002      0.5  -1.64  
     ## # ℹ 93,932 more rows
    @@ -1158,23 +1158,23 @@

    Table {gsm} - thresh: -2 0.00 -15360 -207 +15347 +220 90 15657 -0.013 +0.014 fpr 0.012 -0.015 +0.016 {gsm} - thresh: -2 0.10 -14618 -949 +14621 +946 90 15657 -0.061 +0.060 tpr 0.057 0.064 @@ -1182,122 +1182,122 @@

    Table {gsm} - thresh: -2 0.25 -11694 -3874 +11617 +3951 89 15657 -0.247 +0.252 tpr -0.241 -0.254 +0.246 +0.259 {gsm} - thresh: -2 0.50 -6946 -8624 -87 +6875 +8694 +88 15657 -0.551 +0.555 tpr -0.543 -0.559 +0.547 +0.563 {gsm} - thresh: -2 0.75 -4205 -11365 +4164 +11406 87 15657 -0.726 +0.728 tpr -0.719 -0.733 +0.721 +0.735 {gsm} - thresh: -2 1.00 -2652 -12919 +2600 +12971 86 15657 -0.825 +0.828 tpr -0.819 -0.831 +0.822 +0.834 {simaerep} - thresh: 0.95 0.00 -15472 -202 +15480 +194 0 15674 -0.013 +0.012 fpr 0.011 -0.015 +0.014 {simaerep} - thresh: 0.95 0.10 -14792 -882 +14762 +912 0 15674 -0.056 +0.058 tpr -0.053 -0.060 +0.055 +0.062 {simaerep} - thresh: 0.95 0.25 -12416 -3258 +12278 +3396 0 15674 -0.208 +0.217 tpr -0.202 -0.214 +0.210 +0.223 {simaerep} - thresh: 0.95 0.50 -7907 -7767 +7815 +7859 0 15674 -0.496 +0.501 tpr -0.488 -0.503 +0.494 +0.509 {simaerep} - thresh: 0.95 0.75 -4830 -10844 +4627 +11047 0 15674 -0.692 +0.705 tpr -0.685 -0.699 +0.698 +0.712 {simaerep} - thresh: 0.95 1.00 -3570 -12104 +3416 +12258 0 15674 -0.772 +0.782 tpr -0.766 -0.779 +0.775 +0.788 @@ -1346,15 +1346,16 @@

    Analysis
     df_qu <- df_portf %>%
       summarize(n_pat = n_distinct(patnum), .by = c("study_id", "site_number", "ae_per_visit_mean")) %>%
    -  left_join(
    +  inner_join(
         df_sim %>%
           select(study_id, site_number = siteid, ur_rate, score)
       ) %>%
    -  left_join(
    +  inner_join(
         df_sim_simaerep %>%
           select(study_id, site_number, ur_rate, prob_low_prob_ur)
       ) %>%
       mutate(
    +    score = ifelse(is.na(score), 0 , score),
         flag = case_when(
           score <= -2 & prob_low_prob_ur >= 0.95 ~ "double",
           score <= -2 ~ "gsm only",
    @@ -1370,7 +1371,7 @@ 

    Analysis ggplot(aes(log(n_pat), ae_per_visit_mean, colour = flag)) + geom_point(alpha = 0.25) + theme(legend.position = "bottom", aspect.ratio = 1) + - labs(x = "Log Number of Patiends per Site", y = "AE per Visit Study Average", title = paste("Under Rporting Rate:", ur_rate)) + labs(x = "Log Number of Patiends per Site", y = "AE per Visit Study Average", title = paste("Under Reporting Rate:", ur_rate)) ggExtra::ggMarginal(p, groupColour = TRUE, groupFill = TRUE) @@ -1386,12 +1387,12 @@

    Analysis ggplot(aes(log(n_pat), ae_per_visit_mean, colour = flag)) + geom_point(alpha = 0.25) + theme(legend.position = "bottom", aspect.ratio = 1) + - labs(x = "Log Number of Patiends per Site", y = "AE per Visit Study Average", title = paste("Fals Positive Flags")) + labs(x = "Log Number of Patiends per Site", y = "AE per Visit Study Average", title = paste("False Positive Flags")) p_fpr <- ggExtra::ggMarginal(p_fpr, groupColour = TRUE, groupFill = TRUE) gridExtra::grid.arrange(p_p5, p_p75, p_1, p_fpr, ncol = 2)

    -

    +

    Combine both approaches @@ -1401,26 +1402,24 @@

    Combine both approaches
    -cut_off <- 1
    -
    -df_comb1 <- df_qu %>%
    +df_comb1 <- df_qu %>%
       mutate(
         type = "comb. ae/visit thresh: 1",
    -    is_ur = (ae_per_visit_mean <= 1 & score <= -2) | ae_per_visit_mean > 1 & prob_low_prob_ur >= 0.95
    +    is_ur = (ae_per_visit_mean <= 1 & score <= -2) | (ae_per_visit_mean > 1 & prob_low_prob_ur >= 0.95)
       ) %>%
       select(type, study_id, site_number, ur_rate, is_ur)
     
     df_combp25 <- df_qu %>%
       mutate(
         type = "comb. ae/visit thresh: 0.25",
    -    is_ur = (ae_per_visit_mean <= 0.25 & score <= -2) | ae_per_visit_mean > 0.25 & prob_low_prob_ur >= 0.95
    +    is_ur = (ae_per_visit_mean <= 0.25 & score <= -2) | (ae_per_visit_mean > 0.25 & prob_low_prob_ur >= 0.95)
       ) %>%
       select(type, study_id, site_number, ur_rate, is_ur)
     
     df_combp5 <- df_qu %>%
       mutate(
         type = "comb. ae/visit thresh: 0.5",
    -    is_ur = (ae_per_visit_mean <= 0.5 & score <= -2) | ae_per_visit_mean > 0.5 & prob_low_prob_ur >= 0.95
    +    is_ur = (ae_per_visit_mean <= 0.5 & score <= -2) | (ae_per_visit_mean > 0.5 & prob_low_prob_ur >= 0.95)
       ) %>%
       select(type, study_id, site_number, ur_rate, is_ur)
     
    @@ -1436,7 +1435,7 @@ 

    Combine both approaches plot_perf(df_comb_perf) + labs(title = "Combined Performance with Differect ")

    -

    +

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select(study_id, site_number, ur_rate, prob_low_prob_ur) ) %>% mutate( + score = ifelse(is.na(score), 0 , score), flag = case_when( score <= -2 & prob_low_prob_ur >= 0.95 ~ "double", score <= -2 ~ "gsm only", @@ -309,7 +311,7 @@ plot_flags <- function(df, ur_rate) { ggplot(aes(log(n_pat), ae_per_visit_mean, colour = flag)) + geom_point(alpha = 0.25) + theme(legend.position = "bottom", aspect.ratio = 1) + - labs(x = "Log Number of Patiends per Site", y = "AE per Visit Study Average", title = paste("Under Rporting Rate:", ur_rate)) + labs(x = "Log Number of Patiends per Site", y = "AE per Visit Study Average", title = paste("Under Reporting Rate:", ur_rate)) ggExtra::ggMarginal(p, groupColour = TRUE, groupFill = TRUE) @@ -325,7 +327,7 @@ p_fpr <- df_qu %>% ggplot(aes(log(n_pat), ae_per_visit_mean, colour = flag)) + geom_point(alpha = 0.25) + theme(legend.position = "bottom", aspect.ratio = 1) + - labs(x = "Log Number of Patiends per Site", y = "AE per Visit Study Average", title = paste("Fals Positive Flags")) + labs(x = "Log Number of Patiends per Site", y = "AE per Visit Study Average", title = paste("False Positive Flags")) p_fpr <- ggExtra::ggMarginal(p_fpr, groupColour = TRUE, groupFill = TRUE) @@ -336,28 +338,26 @@ gridExtra::grid.arrange(p_p5, p_p75, p_1, p_fpr, ncol = 2) - clear performance increase only for studies with > 1 ae per visit rate, which is very rare. -```{r} - -cut_off <- 1 +```{r, fig.width=6, fig.height = 10} df_comb1 <- df_qu %>% mutate( type = "comb. ae/visit thresh: 1", - is_ur = (ae_per_visit_mean <= 1 & score <= -2) | ae_per_visit_mean > 1 & prob_low_prob_ur >= 0.95 + is_ur = (ae_per_visit_mean <= 1 & score <= -2) | (ae_per_visit_mean > 1 & prob_low_prob_ur >= 0.95) ) %>% select(type, study_id, site_number, ur_rate, is_ur) df_combp25 <- df_qu %>% mutate( type = "comb. ae/visit thresh: 0.25", - is_ur = (ae_per_visit_mean <= 0.25 & score <= -2) | ae_per_visit_mean > 0.25 & prob_low_prob_ur >= 0.95 + is_ur = (ae_per_visit_mean <= 0.25 & score <= -2) | (ae_per_visit_mean > 0.25 & prob_low_prob_ur >= 0.95) ) %>% select(type, study_id, site_number, ur_rate, is_ur) df_combp5 <- df_qu %>% mutate( type = "comb. ae/visit thresh: 0.5", - is_ur = (ae_per_visit_mean <= 0.5 & score <= -2) | ae_per_visit_mean > 0.5 & prob_low_prob_ur >= 0.95 + is_ur = (ae_per_visit_mean <= 0.5 & score <= -2) | (ae_per_visit_mean > 0.5 & prob_low_prob_ur >= 0.95) ) %>% select(type, study_id, site_number, ur_rate, is_ur) From f4507363e82e54ab314d8ec2d58a27fbbf0245dd Mon Sep 17 00:00:00 2001 From: Bjoern Koneswarakantha Date: Wed, 21 Feb 2024 16:07:45 +0100 Subject: [PATCH 3/7] test gsm performance for flexible ae rates --- R/simulate_test_data.R | 118 +- docs/articles/gsm_perf.html | 1648 +++++++++-------- .../gsm_perf_files/figure-html/plot-1.png | Bin 115010 -> 154442 bytes .../figure-html/unnamed-chunk-3-1.png | Bin 106488 -> 197645 bytes .../figure-html/unnamed-chunk-4-1.png | Bin 0 -> 407879 bytes .../figure-html/unnamed-chunk-6-1.png | Bin 0 -> 107055 bytes vignettes/gsm_perf.Rmd | 331 ++-- 7 files changed, 1163 insertions(+), 934 deletions(-) create mode 100644 docs/articles/gsm_perf_files/figure-html/unnamed-chunk-4-1.png create mode 100644 docs/articles/gsm_perf_files/figure-html/unnamed-chunk-6-1.png diff --git a/R/simulate_test_data.R b/R/simulate_test_data.R index b4f39ef..4ddd6eb 100644 --- a/R/simulate_test_data.R +++ b/R/simulate_test_data.R @@ -12,6 +12,7 @@ #' @param max_visit_sd standard deviation of maximum number of visits of each #' patient, Default: 4 #' @param ae_per_visit_mean mean ae per visit per patient, Default: 0.5 +#' @param ae_rates vector with visit-specific ae rates, Default: Null #' @return tibble with columns site_number, patnum, is_ur, max_visit_mean, #' max_visit_sd, ae_per_visit_mean, visit, n_ae #' @details maximum visit number will be sampled from normal distribution with @@ -25,6 +26,8 @@ #' df_visit <- sim_test_data_study(n_pat = 100, n_sites = 5, #' frac_site_with_ur = 0.2, ur_rate = 0.5) #' df_visit[which(df_visit$patnum == "P000001"),] +#' ae_rates <- c(0.7, rep(0.5, 8), rep(0.3, 5)) +#' sim_test_data_study(n_pat = 100, n_sites = 5, ae_rates = ae_rates) #' @rdname sim_test_data_study #' @export sim_test_data_study <- function(n_pat = 1000, @@ -33,8 +36,71 @@ sim_test_data_study <- function(n_pat = 1000, ur_rate = 0, max_visit_mean = 20, max_visit_sd = 4, - ae_per_visit_mean = 0.5 + ae_per_visit_mean = 0.5, + ae_rates = NULL ) { + + # construct patient ae sample function + # supports constant and non-constant ae rates + + f_sim_pat <- function(vs_max, vs_sd, is_ur) { + + if (! any(c(is.null(ae_rates), is.na(ae_rates)))) { + + if (is_ur) { + ae_rates <- ae_rates * (1-ur_rate) + } + + f_sample_ae <- function(max_visit) { + + # extrapolate missing ae rates by extending last rate + fill <- rep(ae_rates[length(ae_rates)], max_visit) + fill[1: length(ae_rates)] <- ae_rates + ae_rates <- fill + + aes <- integer(0) + + for (i in seq(1, max_visit)) { + ae <- rpois(1, ae_rates[i]) + aes <- c(aes, ae) + } + + return(aes) + + } + + ae_per_visit_mean <- mean(ae_rates) + + } else { + + if (is_ur) { + ae_per_visit_mean <- ae_per_visit_mean * (1-ur_rate) + } + + f_sample_ae <- function(max_visit) { + + rpois(max_visit, ae_per_visit_mean) + + } + + } + + aes <- sim_test_data_patient( + .f_sample_max_visit = function(x) rnorm(1, mean = vs_max, sd = vs_sd), + .f_sample_ae_per_visit = f_sample_ae + ) + + tibble( + visit = seq(1, length(aes)), + n_ae = aes, + ae_per_visit_mean = ae_per_visit_mean + ) + + } + + + + tibble(patnum = seq(1, n_pat)) %>% mutate(patnum = str_pad(patnum, width = 6, side = "left", pad = "0"), patnum = paste0("P", patnum), @@ -45,18 +111,12 @@ sim_test_data_study <- function(n_pat = 1000, site_number = paste0("S", .data$site_number), max_visit_mean = max_visit_mean, max_visit_sd = max_visit_sd, - ae_per_visit_mean = ifelse(.data$is_ur, ae_per_visit_mean * (1 - ur_rate), ae_per_visit_mean), - aes = pmap(list(vm = max_visit_mean, - vs = max_visit_sd, - am = ae_per_visit_mean), - function(vm, vs, am) { - sim_test_data_patient( - .f_sample_max_visit = function() rnorm(1, mean = vm, sd = vs), - .f_sample_ae_per_visit = function(max_visit) rpois(max_visit, am) - ) - } - ), - aes = map(aes, ~ tibble(visit = seq(1, length(.)), n_ae = .))) %>% + aes = pmap(list(max_visit_mean, + max_visit_sd, + is_ur), + f_sim_pat + ) + ) %>% unnest(aes) } @@ -389,6 +449,7 @@ sim_ur_scenarios <- function(df_portf, #' @title Simulate Portfolio Test Data #' @description Simulate visit level data from a portfolio configuration. #' @param df_config dataframe as returned by \code{\link{get_config}} +#' @param df_ae_rates dataframe with ae rates. Default: NULL #' @param parallel logical activate parallel processing, see details, Default: FALSE #' @param progress logical, Default: TRUE #'@return dataframe with the following columns: \describe{ @@ -452,7 +513,7 @@ sim_ur_scenarios <- function(df_portf, #' \code{\link{get_portf_perf}} #' @rdname sim_test_data_portfolio #' @export -sim_test_data_portfolio <- function(df_config, parallel = FALSE, progress = TRUE) { +sim_test_data_portfolio <- function(df_config, df_ae_rates = NULL, parallel = FALSE, progress = TRUE) { # checks -------------------------- df_config <- ungroup(df_config) @@ -478,6 +539,24 @@ sim_test_data_portfolio <- function(df_config, parallel = FALSE, progress = TRUE ) ) + # prep ae_rates ----------------- + + if (is.null(df_ae_rates)) { + df_config$ae_rates <- NA + } else { + df_ae_rates <- df_ae_rates %>% + select(study_id, ae_rate) %>% + group_by(study_id) %>% + nest() %>% + mutate( + ae_rates = map(data, "ae_rate") + ) %>% + select(- data) + + df_config <- df_config %>% + inner_join(df_ae_rates, by = "study_id") + } + # exec -------------------------- if (parallel) { @@ -496,19 +575,22 @@ sim_test_data_portfolio <- function(df_config, parallel = FALSE, progress = TRUE .data$ae_per_visit_mean, .data$max_visit_sd, .data$max_visit_mean, - .data$n_pat + .data$n_pat, + .data$ae_rates ), .purrr = .purrr, .f = function(ae_per_visit_mean, max_visit_sd, max_visit_mean, - n_pat) { + n_pat, + ae_rates) { sim_test_data_study( n_pat = n_pat, n_sites = 1, max_visit_mean = max_visit_mean, max_visit_sd = max_visit_sd, - ae_per_visit_mean = ae_per_visit_mean + ae_per_visit_mean = ae_per_visit_mean, + ae_rates = ae_rates ) %>% select( # using rlang::.data here causes error with furrr @@ -525,7 +607,7 @@ sim_test_data_portfolio <- function(df_config, parallel = FALSE, progress = TRUE df_portf <- df_config_sim %>% unnest(.data$sim) %>% - select(- .data$n_pat) %>% + select(- c("n_pat" ,"ae_rates")) %>% group_by(.data$study_id) %>% mutate( # patnums need to be made site exclusive diff --git a/docs/articles/gsm_perf.html b/docs/articles/gsm_perf.html index df01c6a..df08146 100644 --- a/docs/articles/gsm_perf.html +++ b/docs/articles/gsm_perf.html @@ -46,7 +46,7 @@ simaerep - 0.4.3 + 0.4.4
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    @@ -140,8 +140,6 @@

    Load suppressPackageStartupMessages(library(furrr)) suppressPackageStartupMessages(library(future)) suppressPackageStartupMessages(library(simaerep)) -suppressPackageStartupMessages(library(ggExtra)) -suppressPackageStartupMessages(library(gridExtra)) plan(multisession, workers = 6) @@ -164,6 +162,9 @@

    IntroductionZink et al. 2018

    +

    One of the draw backs of using funnel plots for flagging is that they +assume that the AE rate remains constant over the course of the +study.

    Prepare Data @@ -171,310 +172,398 @@

    Prepare Data

    Load Portfolio Configurations

    +

    We have prepared a snapshot of the AE reporting configuration of our +current portfolio. For each study we have also measured a visit-specific +AE rate which allows us to generate a synthetic portfolio with flexible +AE rates across a study.

    -df_config <- readr::read_csv("ae_profile.csv")
    +df_config <- readr::read_csv("ae_conf_20240220.csv")
    +df_ae_rates <- readr::read_csv("ae_rates_20240220.csv")
     
     df_config %>%
       head(25) %>%
       knitr::kable()
    - +
    --------+++++ - - - - - - - - - - + + + + - - - - - - - + + + + - - - - - - - + + + + - - - - - - - + + + + - - - + + - - - + - - - + + - - - + - - - + + - - - + - - - - - - - + + + + - - - + + - - - - - - - - - + + + + - - - + + - - - - - + + - - - + - - - + + - - - - - + + - - - - - - - - - + + + + - - - + + - - - + - - - - - - - + + + + - - - - - - - + + + + - - - - - - - + + + + - - - + + - - - + - - - - - - - + + + + - - - - - - - + + + + - - - + + - - - - - + + - - - + - - - - - - - + + + + + + + +
    study_id ae_per_visit_meanae_per_day_mean site_number max_visit_sd max_visit_meanmax_days_sdmax_days_mean n_pat
    00010.26870870.007249200010.707106822.500006226220.297380647463.535533916.50000 2
    00010.26870870.007249200022.121320328.50000135013500.297380647470.707106827.50000 2
    00010.26870870.007249200030.577350319.500007387380.297380647500.500000019.25000 4
    00010.26870870.007249200048.869423131.33333146114610.297380648158.376554622.16667 6
    00010.26870870.007249200050.29738064816 0.000000030.000001253125327.00000 1
    00010.26870870.007249200060.29738064817 0.000000035.000001479147931.00000 1
    00010.26870870.007249200070.29738064818 0.000000036.000001415141530.00000 1
    00010.26870870.007249200085.336308726.142869719710.297380648912.380476122.00000 7
    00010.26870870.007249200090.29738064893 0.0000000 20.00000756756 2
    00010.26870870.007249200101.816590227.40000136413640.297380649321.095445126.80000 5
    00010.26870870.007249200110.29738064941 1.7320508 27.0000011901190 3
    00010.26870870.007249200120.29738064942 1.414213626.000001186118625.00000 2
    00010.26870870.007249200130.29738064943 0.0000000 25.0000011271127 1
    00010.26870870.007249200140.29738064966 0.0000000 18.00000566566 1
    00010.26870870.007249200151.707825118.750005885880.297380649670.816496618.00000 4
    00010.26870870.007249200160.29738064968 0.000000025.0000064464424.00000 1
    00010.26870870.007249200171.000000022.000008278270.297380649691.527525221.33333 3
    00010.26870870.007249200184.041451932.50000153415340.297380649841.707825130.75000 4
    00010.26870870.0072492001912.940891325.33333118311830.297380649858.908797221.16667 6
    00010.26870870.007249200200.29738064986 0.577350320.6666777077020.33333 3
    00010.26870870.0072492002114.849242428.500006666660.297380649881.414213619.00000 2
    00010.26870870.007249200229.036961126.500009879870.297380650792.160246923.00000 4
    00010.26870870.007249200230.29738065080 0.0000000 23.00000931931 1
    00010.26870870.007249200240.29738065081 0.000000038.0000093893823.00000 1
    00010.26870870.007249200256.363961026.500009389380.297380650821.414213622.000002
    +
    +df_ae_rates %>%
    +  head(25) %>%
    +  knitr::kable()
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    study_idcum_visitae_raten_pat
    000110.017699113
    0001 20.088496113
    000130.230088113
    000140.223214112
    000150.187500112
    000160.196429112
    000170.396396111
    000180.207207111
    000190.216216111
    0001100.315315111
    0001110.263636110
    0001120.318182110
    0001130.345455110
    0001140.379630108
    0001150.401869107
    0001160.485981107
    0001170.383178107
    0001180.396226106
    0001190.370000100
    0001200.43956091
    0001210.36986373
    0001220.37096862
    0001230.34545555
    0001240.44680947
    0001250.29268341
    @@ -482,38 +571,32 @@

    Load Portfolio Configurations

    Simulate Portfolio

    -

    And again we simulate artificial visit level data. Using parallel -processing. At this stage we have simulated compliant test data of a -realistic study portfolio.

    -
    -df_portf <- sim_test_data_portfolio(df_config, parallel = TRUE, progress = TRUE)
    +

    We generate two synthetic portfolios with no AE under-reporting +sites. One portfolio with a fixed AE rate for all visits and another one +with a flexible visit-specific AE rate.

    +
    +df_portf_fix <- sim_test_data_portfolio(df_config, parallel = TRUE, progress = TRUE)
     
    -df_portf %>%
    +df_portf_fix %>%
       head(25) %>%
       knitr::kable()
    -------++++++-- - - - @@ -521,331 +604,294 @@

    Simulate Portfolio

    - - - - - - - + + + + - - - - - - - + + + + - + - - - - - - - + + + + - - - - - - - + + + + - + - - - - - - - + + + + - + - - - - - - - + + + + - + - - - - - - - + + + + - - - - - - - + + + + - - - - - - - + + + + - - - - - - - + + + + - + - - - - - - - + + + + - + - - - - - - - + + + + - + - - - - - - - + + + + - + - - - - - - - + + + + - + - - - - - - - + + + + - + - - - - - - - + + + + - + - - - - - - - + + + + - + - - - - - - - + + + + - + - - - - - - - + + + + - + - - - - - - - - - - + + + + + + + - - - - - - - - - - + + + + + + + - - - - - - - - - - + + + + + + + - - - - - - - + + + + - - + + - - - - - - - + + + + + - - - - - - - - + + + + - +
    study_id ae_per_visit_meanae_per_day_mean site_number max_visit_sd max_visit_meanmax_days_sdmax_days_mean patnum visit n_ae00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 0001 1 0
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 0001 201
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 0001 3 1
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 0001 421
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 0001 521
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 0001 621
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 0001 7 2
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 0001 8 2
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 0001 9 2
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 0001 1032
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 0001 1142
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 0001 1242
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 0001 1343
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 0001 1453
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 0001 1553
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 0001 1653
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 0001 1753
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 0001 1853
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 0001 1953
    00010.26870870.007249200010.707106822.562262200012050.297380647463.53553416.5000211
    00010.26870870.007249200010.707106822.562262200012150.297380647463.53553416.5000221
    00010.26870870.007249200010.707106822.562262200012250.297380647463.53553416.5000232
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 00021042
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 00025 20
    00010.26870870.007249200010.707106822.56226220.297380647463.53553416.5 000236 2
    +
    +df_portf_flex <- sim_test_data_portfolio(df_config, df_ae_rates = df_ae_rates, parallel = TRUE, progress = TRUE)
    +
    +
    +

    Compare AE rates +

    +

    Next we confirm the different AE rates in our two synthetic +portfolios.

    +
    +df_rate_fix <- df_portf_fix %>%
    +  mutate(ae_rate = coalesce(n_ae - lag(n_ae), n_ae), .by = c("study_id", "patnum")) %>%
    +  summarise(ae_rate = mean(ae_rate), .by = c("study_id", "visit")) %>%
    +  mutate(rate = "fix")
    +
    +
    +df_rate_flex <- df_portf_flex %>%
    +  mutate(ae_rate = coalesce(n_ae - lag(n_ae), n_ae), .by = c("study_id", "patnum")) %>%
    +  summarise(ae_rate = mean(ae_rate), .by = c("study_id", "visit")) %>%
    +  mutate(rate = "flex")
    +
    +bind_rows(df_rate_flex, df_rate_fix) %>%
    +  ggplot(aes(visit, ae_rate)) +
    +    geom_line(aes(group = study_id), alpha = 0.2) +
    +    geom_smooth() +
    +    facet_wrap(~ rate) +
    +    labs(title = "Average AE rates per Study")
    +

    +
    +bind_rows(df_rate_flex, df_rate_fix) %>%
    +  filter(dense_rank(study_id) <= 16) %>%
    +  ggplot(aes(visit, ae_rate)) +
    +    geom_line(aes(group = rate, color = rate)) +
    +    facet_wrap(~ study_id, scales = "free") +
    +    labs(title = "Average AE rates for Selected Studies")
    +

    +

    We can confirm that the AE rates in the “flexible” portfolio are not +constant. Moreover we see that the AE rate profile is very unique for +each study.

    @@ -854,40 +900,50 @@

    Apply {gsm}

    Example

    -
    -dfSUBJ <- df_portf %>%
    -  select(study_id, siteid = site_number, subjid = patnum, timeonstudy = visit) %>%
    -  summarise(timeonstudy = max(timeonstudy), .by = c(study_id, siteid, subjid)) %>%
    -  group_by(study_id) %>%
    -  nest()
    +

    Here we demonstrate how to use the {gsm} package on our simulated +portfolios.

    +
    +get_SUBJ <- function(df_portf) {
    +  df_portf %>%
    +    select(study_id, siteid = site_number, subjid = patnum, timeonstudy = visit) %>%
    +    summarise(timeonstudy = max(timeonstudy), .by = c(study_id, siteid, subjid)) %>%
    +    group_by(study_id) %>%
    +    nest()
    +}
     
    -dfAE <- df_portf %>%
    -  select(study_id, subjid = patnum, n_ae) %>%
    -  summarise(n_ae = max(n_ae), .by = c(study_id, subjid)) %>%
    -  filter(n_ae > 0) %>%
    -  mutate(n_ae = map(n_ae, ~ tibble(n = seq(1, .)), .progress = TRUE)) %>%
    -  unnest(n_ae) %>%
    -  select(- n) %>%
    -  group_by(study_id) %>%
    -  nest()
    -
    -dfInput <- gsm::AE_Map_Raw(list(dfSUBJ = dfSUBJ$data[[1]], dfAE = dfAE$data[[1]]))
    +
    +get_AE <- function(df_portf) {
    +  df_portf_fix %>%
    +    select(study_id, subjid = patnum, n_ae) %>%
    +    summarise(n_ae = max(n_ae), .by = c(study_id, subjid)) %>%
    +    filter(n_ae > 0) %>%
    +    mutate(n_ae = map(n_ae, ~ tibble(n = seq(1, .)), .progress = TRUE)) %>%
    +    unnest(n_ae) %>%
    +    select(- n) %>%
    +    group_by(study_id) %>%
    +    nest()
    +}
    +
    +dfSUBJ_fix <- get_SUBJ(df_portf_fix)
    +dfAE_fix <- get_AE(df_portf_fix)
    +
    +dfInput <- gsm::AE_Map_Raw(list(dfSUBJ = dfSUBJ_fix$data[[1]], dfAE = dfAE_fix$data[[1]]))
     dfInput
    ## # A tibble: 113 × 5
    -##    SubjectID SiteID Exposure Count   Rate
    -##    <chr>     <chr>     <int> <int>  <dbl>
    -##  1 0001      0001         22     5 0.227 
    -##  2 0002      0001         22     5 0.227 
    -##  3 0003      0002         30     9 0.3   
    -##  4 0004      0002         28     8 0.286 
    -##  5 0005      0003         17     5 0.294 
    -##  6 0006      0003         19     5 0.263 
    -##  7 0007      0003         19     1 0.0526
    -##  8 0008      0003         19     3 0.158 
    -##  9 0009      0004         31     4 0.129 
    -## 10 0010      0004         30     1 0.0333
    +##    SubjectID SiteID Exposure Count  Rate
    +##    <chr>     <chr>     <int> <int> <dbl>
    +##  1 0001      4746         19     3 0.158
    +##  2 0002      4746         24     8 0.333
    +##  3 0003      4747         26     6 0.231
    +##  4 0004      4747         28     7 0.25 
    +##  5 0005      4750         18     7 0.389
    +##  6 0006      4750         18     3 0.167
    +##  7 0007      4750         19     5 0.263
    +##  8 0008      4750         19     5 0.263
    +##  9 0009      4815          3     2 0.667
    +## 10 0010      4815         18     3 0.167
     ## # ℹ 103 more rows
    -
    +
     dfTransformed <- gsm::Transform_Rate(
       dfInput,
       strNumeratorCol = "Count",
    @@ -897,95 +953,97 @@ 

    Example## # A tibble: 44 × 4 ## GroupID Numerator Denominator Metric ## <chr> <int> <int> <dbl> -## 1 0001 10 44 0.227 -## 2 0002 17 58 0.293 -## 3 0003 14 74 0.189 -## 4 0004 39 173 0.225 -## 5 0005 14 30 0.467 -## 6 0006 16 35 0.457 -## 7 0007 10 36 0.278 -## 8 0008 41 180 0.228 -## 9 0009 12 40 0.3 -## 10 0010 41 132 0.311 +## 1 4746 11 43 0.256 +## 2 4747 13 54 0.241 +## 3 4750 20 74 0.270 +## 4 4815 16 77 0.208 +## 5 4816 12 27 0.444 +## 6 4817 7 31 0.226 +## 7 4818 14 30 0.467 +## 8 4891 59 149 0.396 +## 9 4893 9 40 0.225 +## 10 4932 41 131 0.313 ## # ℹ 34 more rows

    -
    +
     
    ## # A tibble: 44 × 7
     ##    GroupID Numerator Denominator Metric OverallMetric Factor  Score
     ##    <chr>       <int>       <int>  <dbl>         <dbl>  <dbl>  <dbl>
    -##  1 0003           14          74  0.189         0.270   1.49 -1.28 
    -##  2 0039            7          42  0.167         0.270   1.49 -1.24 
    -##  3 0019           44         197  0.223         0.270   1.49 -1.21 
    -##  4 0004           39         173  0.225         0.270   1.49 -1.08 
    -##  5 0026            8          44  0.182         0.270   1.49 -1.08 
    -##  6 0008           41         180  0.228         0.270   1.49 -1.05 
    -##  7 0021           19          88  0.216         0.270   1.49 -0.936
    -##  8 0017           14          67  0.209         0.270   1.49 -0.922
    -##  9 0023            4          23  0.174         0.270   1.49 -0.850
    -## 10 0027            4          21  0.190         0.270   1.49 -0.672
    +##  1 5311            1          20 0.05           0.296   1.27 -2.14 
    +##  2 5084            2          21 0.0952         0.296   1.27 -1.79 
    +##  3 4815           16          77 0.208          0.296   1.27 -1.51 
    +##  4 5111            5          31 0.161          0.296   1.27 -1.46 
    +##  5 5229           13          61 0.213          0.296   1.27 -1.26 
    +##  6 5131            4          23 0.174          0.296   1.27 -1.14 
    +##  7 5308           14          61 0.230          0.296   1.27 -1.01 
    +##  8 4942           12          53 0.226          0.296   1.27 -0.987
    +##  9 5166           24          98 0.245          0.296   1.27 -0.986
    +## 10 4893            9          40 0.225          0.296   1.27 -0.875
     ## # ℹ 34 more rows
    -
    +
     dfFlagged <- gsm::Flag_NormalApprox(dfAnalyzed, vThreshold = c(-3, -2, 2, 3))
     dfFlagged
    ## # A tibble: 44 × 8
     ##    GroupID Numerator Denominator Metric OverallMetric Factor  Score  Flag
     ##    <chr>       <int>       <int>  <dbl>         <dbl>  <dbl>  <dbl> <dbl>
    -##  1 0006           16          35  0.457         0.270   1.49  2.04      1
    -##  2 0032           14          26  0.538         0.270   1.49  2.53      1
    -##  3 0003           14          74  0.189         0.270   1.49 -1.28      0
    -##  4 0039            7          42  0.167         0.270   1.49 -1.24      0
    -##  5 0019           44         197  0.223         0.270   1.49 -1.21      0
    -##  6 0004           39         173  0.225         0.270   1.49 -1.08      0
    -##  7 0026            8          44  0.182         0.270   1.49 -1.08      0
    -##  8 0008           41         180  0.228         0.270   1.49 -1.05      0
    -##  9 0021           19          88  0.216         0.270   1.49 -0.936     0
    -## 10 0017           14          67  0.209         0.270   1.49 -0.922     0
    +##  1 4891           59         149 0.396          0.296   1.27  2.37      1
    +##  2 5311            1          20 0.05           0.296   1.27 -2.14     -1
    +##  3 5084            2          21 0.0952         0.296   1.27 -1.79      0
    +##  4 4815           16          77 0.208          0.296   1.27 -1.51      0
    +##  5 5111            5          31 0.161          0.296   1.27 -1.46      0
    +##  6 5229           13          61 0.213          0.296   1.27 -1.26      0
    +##  7 5131            4          23 0.174          0.296   1.27 -1.14      0
    +##  8 5308           14          61 0.230          0.296   1.27 -1.01      0
    +##  9 4942           12          53 0.226          0.296   1.27 -0.987     0
    +## 10 5166           24          98 0.245          0.296   1.27 -0.986     0
     ## # ℹ 34 more rows
    -
    +
     dfSummary <- gsm::Summarize(dfFlagged)
     dfSummary
    ## # A tibble: 44 × 6
    -##    GroupID Numerator Denominator Metric Score  Flag
    -##    <chr>       <int>       <int>  <dbl> <dbl> <dbl>
    -##  1 0032           14          26  0.538 2.53      1
    -##  2 0006           16          35  0.457 2.04      1
    -##  3 0044           10          20  0.5   1.90      0
    -##  4 0030           12          25  0.48  1.94      0
    -##  5 0005           14          30  0.467 1.99      0
    -##  6 0013           10          25  0.4   1.20      0
    -##  7 0014            7          18  0.389 0.931     0
    -##  8 0037           11          30  0.367 0.977     0
    -##  9 0040           14          39  0.359 1.03      0
    -## 10 0042           17          50  0.34  0.914     0
    +##    GroupID Numerator Denominator Metric  Score  Flag
    +##    <chr>       <int>       <int>  <dbl>  <dbl> <dbl>
    +##  1 4891           59         149  0.396  2.37      1
    +##  2 5311            1          20  0.05  -2.14     -1
    +##  3 4818           14          30  0.467  1.81      0
    +##  4 5135            8          18  0.444  1.22      0
    +##  5 4816           12          27  0.444  1.50      0
    +##  6 4969           25          62  0.403  1.64      0
    +##  7 5309           16          42  0.381  1.07      0
    +##  8 5265           18          48  0.375  1.06      0
    +##  9 5082           16          43  0.372  0.967     0
    +## 10 4967           25          68  0.368  1.15      0
     ## # ℹ 34 more rows
    -
    +
     dfBounds <- gsm::Analyze_NormalApprox_PredictBounds(dfTransformed, vThreshold = c(-3, -2, 2, 3))
     dfBounds
    -
    ## # A tibble: 1,238 × 5
    +
    ## # A tibble: 1,250 × 5
     ##    Threshold Denominator LogDenominator Numerator  Metric
     ##        <dbl>       <dbl>          <dbl>     <dbl>   <dbl>
    -##  1        -3        36.6           3.60    0.0492 0.00134
    -##  2        -3        37.3           3.62    0.147  0.00393
    -##  3        -3        38.0           3.64    0.245  0.00645
    -##  4        -3        38.8           3.66    0.345  0.00889
    -##  5        -3        39.5           3.68    0.445  0.0113 
    -##  6        -3        40.2           3.69    0.546  0.0136 
    -##  7        -3        40.9           3.71    0.648  0.0158 
    -##  8        -3        41.6           3.73    0.751  0.0180 
    -##  9        -3        42.3           3.75    0.854  0.0202 
    -## 10        -3        43.1           3.76    0.959  0.0223 
    -## # ℹ 1,228 more rows
    -
    +##  1        -3        27.4           3.31    0.0339 0.00124
    +##  2        -3        28.1           3.34    0.134  0.00479
    +##  3        -3        28.8           3.36    0.236  0.00821
    +##  4        -3        29.4           3.38    0.339  0.0115 
    +##  5        -3        30.1           3.40    0.443  0.0147 
    +##  6        -3        30.8           3.43    0.548  0.0178 
    +##  7        -3        31.4           3.45    0.654  0.0208 
    +##  8        -3        32.1           3.47    0.761  0.0237 
    +##  9        -3        32.8           3.49    0.869  0.0265 
    +## 10        -3        33.5           3.51    0.978  0.0292 
    +## # ℹ 1,240 more rows
    +
     chart <- gsm::Visualize_Scatter(dfFlagged, dfBounds)
     chart
    -

    +

    Simulate UR

    -
    -sim_gsm_ur <- function(site, ur_rate, dfTransformed) {
    +

    We write a function that removes a given ratio of AEs from one site +in the data set and returns its z-score.

    +
    +sim_site_ur_gsm <- function(site, ur_rate, dfTransformed) {
       dfTransformed <- dfTransformed %>%
         mutate(
           Numerator = ifelse(GroupID == site, Numerator * (1 - ur_rate), Numerator),
    @@ -997,25 +1055,32 @@ 

    Simulate URpull(Score) } -sim_gsm_ur("0001", ur_rate = 0.75, dfTransformed)

    -
    ## [1] -2.406863
    -
    -df_sim <- dfSUBJ %>%
    -  inner_join(dfAE, by = "study_id") %>%
    -  ungroup() %>%
    -  mutate(
    -    trans = map2(data.x, data.y, ~ gsm::AE_Map_Raw(list(dfSUBJ = .x, dfAE = .y))),
    -    trans = map(trans, ~ gsm::Transform_Rate(., strNumeratorCol = "Count", strDenominatorCol = "Exposure")),
    -    sites = map(data.x, ~ distinct(., siteid))
    -  ) %>%
    -  select(- starts_with("data.")) %>%
    -  unnest(sites) %>%
    -  mutate(ur = list(tibble(ur_rate = c(0, 0.1, 0.25, 0.5, 0.75, 1)))) %>%
    -  unnest(ur) %>%
    -  mutate(
    -    score = pmap_dbl(list(siteid, ur_rate, trans), sim_gsm_ur, .progress = TRUE)
    -  )
    -
    ## Warning: There were 530 warnings in `mutate()`.
    +sim_site_ur_gsm("4747", ur_rate = 0.75, dfTransformed)
    +
    ## [1] -2.975356
    +

    We write another function that systematically applies this +sim_site_ur_gsm across all sites in all studies across a +range of under-reporting ratios.

    +
    +sim_ur_gsm <- function(dfSUBJ, dfAE) {
    +  dfSUBJ %>%
    +    inner_join(dfAE, by = "study_id") %>%
    +    ungroup() %>%
    +    mutate(
    +      trans = map2(data.x, data.y, ~ gsm::AE_Map_Raw(list(dfSUBJ = .x, dfAE = .y))),
    +      trans = map(trans, ~ gsm::Transform_Rate(., strNumeratorCol = "Count", strDenominatorCol = "Exposure")),
    +      sites = map(data.x, ~ distinct(., siteid))
    +    ) %>%
    +    select(- starts_with("data.")) %>%
    +    unnest(sites) %>%
    +    mutate(ur = list(tibble(ur_rate = c(0, 0.1, 0.25, 0.5, 0.75, 1)))) %>%
    +    unnest(ur) %>%
    +    mutate(
    +      score = pmap_dbl(list(siteid, ur_rate, trans), sim_site_ur_gsm, .progress = TRUE)
    +    )
    +}
    +
    +df_sim_gsm_fix <- sim_ur_gsm(dfSUBJ_fix, dfAE_fix)
    +
    ## Warning: There were 3111 warnings in `mutate()`.
     ## The first warning was:
     ##  In argument: `z_i = ifelse(...)`.
     ## Caused by warning:
    @@ -1023,31 +1088,59 @@ 

    Simulate UR## In argument: `z_0 = ifelse(...)`. ## Caused by warning in `sqrt()`: ## ! NaNs produced -## Run `dplyr::last_dplyr_warnings()` to see the 529 remaining warnings.

    -
    -df_sim
    -
    ## # A tibble: 93,942 × 5
    -##    study_id trans             siteid ur_rate   score
    -##    <chr>    <list>            <chr>    <dbl>   <dbl>
    -##  1 0001     <tibble [44 × 4]> 0001      0    -0.523 
    -##  2 0001     <tibble [44 × 4]> 0001      0.1  -0.794 
    -##  3 0001     <tibble [44 × 4]> 0001      0.25 -1.19  
    -##  4 0001     <tibble [44 × 4]> 0001      0.5  -1.82  
    -##  5 0001     <tibble [44 × 4]> 0001      0.75 -2.41  
    -##  6 0001     <tibble [44 × 4]> 0001      1    -2.93  
    -##  7 0001     <tibble [44 × 4]> 0002      0     0.325 
    -##  8 0001     <tibble [44 × 4]> 0002      0.1  -0.0789
    -##  9 0001     <tibble [44 × 4]> 0002      0.25 -0.681 
    -## 10 0001     <tibble [44 × 4]> 0002      0.5  -1.64  
    -## # ℹ 93,932 more rows
    +## Run `dplyr::last_dplyr_warnings()` to see the 3110 remaining warnings.
    +
    +df_sim_gsm_fix
    +
    ## # A tibble: 129,966 × 5
    +##    study_id trans             siteid ur_rate  score
    +##    <chr>    <list>            <chr>    <dbl>  <dbl>
    +##  1 0001     <tibble [44 × 4]> 4746      0    -0.514
    +##  2 0001     <tibble [44 × 4]> 4746      0.1  -0.830
    +##  3 0001     <tibble [44 × 4]> 4746      0.25 -1.29 
    +##  4 0001     <tibble [44 × 4]> 4746      0.5  -2.02 
    +##  5 0001     <tibble [44 × 4]> 4746      0.75 -2.67 
    +##  6 0001     <tibble [44 × 4]> 4746      1    -3.24 
    +##  7 0001     <tibble [44 × 4]> 4747      0    -0.791
    +##  8 0001     <tibble [44 × 4]> 4747      0.1  -1.12 
    +##  9 0001     <tibble [44 × 4]> 4747      0.25 -1.59 
    +## 10 0001     <tibble [44 × 4]> 4747      0.5  -2.33 
    +## # ℹ 129,956 more rows
    +

    We repeat the same steps for the portfolio with the flexible AE +rates.

    +
    +dfSUBJ_flex <- get_SUBJ(df_portf_flex)
    +dfAE_flex<- get_AE(df_portf_flex)
    +
    +
    +df_sim_gsm_flex <- sim_ur_gsm(dfSUBJ_flex, dfAE_flex)
    +
    ## Warning: There were 3079 warnings in `mutate()`.
    +## The first warning was:
    +##  In argument: `z_i = ifelse(...)`.
    +## Caused by warning:
    +## ! There was 1 warning in `mutate()`.
    +##  In argument: `z_0 = ifelse(...)`.
    +## Caused by warning in `sqrt()`:
    +## ! NaNs produced
    +##  Run `dplyr::last_dplyr_warnings()` to see the 3078 remaining warnings.

    UR {simaerep}

    -
    -df_sim_simaerep <- sim_ur_scenarios(
    -   df_portf,
    +

    We simulate under-reporting for both portfolios using {simaerep}

    +
    +df_sim_simaerep_fix <- sim_ur_scenarios(
    +   df_portf_fix,
    +   extra_ur_sites = 0,
    +   ur_rate = c(0, 0.1, 0.25, 0.5, 0.75, 1),
    +   parallel = TRUE,
    +   poisson = TRUE,
    +   prob_lower = TRUE,
    +   progress = TRUE
    +)
    +
    +df_sim_simaerep_flex <- sim_ur_scenarios(
    +   df_portf_flex,
        extra_ur_sites = 0,
        ur_rate = c(0, 0.1, 0.25, 0.5, 0.75, 1),
        parallel = TRUE,
    @@ -1062,22 +1155,27 @@ 

    Evaluate

    Combine Results

    -
    -df_sim_thresh2 <- df_sim %>%
    +
    +df_sim_gsm_fix$ae_rate <- "AE rate: fix"
    +df_sim_gsm_flex$ae_rate <- "AE rate: flexible"
    +df_sim_simaerep_fix$ae_rate <- "AE rate: fix"
    +df_sim_simaerep_flex$ae_rate <- "AE rate: flexible"
    +
    +df_sim_thresh2 <- bind_rows(df_sim_gsm_fix, df_sim_gsm_flex) %>%
       mutate(
         is_ur = score <= -2,
         type = "{gsm} - thresh: -2",
         site_number = siteid
       ) %>%
    -  select(type, study_id, site_number, ur_rate, is_ur, score)
    +  select(type, ae_rate, study_id, site_number, ur_rate, is_ur, score)
     
     
    -df_sim_simaerep_threshp95 <- df_sim_simaerep %>%
    +df_sim_simaerep_threshp95 <-  bind_rows(df_sim_simaerep_fix, df_sim_simaerep_flex) %>%
       mutate(
         is_ur = prob_low_prob_ur >= 0.95,
         type = "{simaerep} - thresh: 0.95"
       ) %>%
    -  select(type, study_id, site_number, ur_rate, is_ur, score = prob_low_prob_ur)
    +  select(type, ae_rate, study_id, site_number, ur_rate, is_ur, score = prob_low_prob_ur)
     
     
     df_eval <- bind_rows(
    @@ -1088,7 +1186,7 @@ 

    Combine Results

    Aggregate

    -
    +
     get_prop_test_ci95 <- function(..., ix) {
       
       stopifnot(ix %in% c(1, 2))
    @@ -1104,7 +1202,7 @@ 

    Aggregatedf_perf <- df_eval %>% summarise( n = n(), - .by = c(type, ur_rate, is_ur) + .by = c(type, ae_rate, ur_rate, is_ur) ) %>% pivot_wider( names_from = is_ur, @@ -1123,27 +1221,33 @@

    Aggregatedf_perf <- aggr_results(df_eval)

    +

    +
    +

    Results +

    Table

    -
    +
     df_perf %>%
       knitr::kable(digits = 3)
    ----++++--++-++ + @@ -1157,147 +1261,315 @@

    Table

    + - - - - - + + + + + - + + - - - - - + + + + + - - + + + - - - - - + + + + + - - + + + - - - - + + + + - - + + + - - - - - + + + + + - - + + + - - - - - + + + + + - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - + + - - + + + - - + + - - + + - - + + + - - + + - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - + + + - - + + - - + + - - + + + - - + + - - + + - - + + + - - + + - - + + - - + +
    typeae_rate ur_rate is_ur_FALSE is_ur_TRUE
    {gsm} - thresh: -2AE rate: fix 0.001534722090156570.01420848281532216610.013 fpr 0.0120.0160.015
    {gsm} - thresh: -2AE rate: fix 0.101462194690156570.060199231206532216610.056 tpr0.0570.0640.0530.059
    {gsm} - thresh: -2AE rate: fix 0.2511617395189156570.252157625373526216610.248 tpr0.2460.2590.2420.254
    {gsm} - thresh: -2AE rate: fix 0.5068758694881565791271201651821661 0.555 tpr0.5470.5630.5480.561
    {gsm} - thresh: -2AE rate: fix 0.7541641140687156570.728515515998508216610.739 tpr0.7210.7350.7330.744
    {gsm} - thresh: -2AE rate: fix 1.0026001297186156570.828293818228495216610.842 tpr0.8220.8340.8370.846
    {gsm} - thresh: -2AE rate: flexible0.0020901239521216610.011fpr0.0100.013
    {gsm} - thresh: -2AE rate: flexible0.1020665475521216610.022tpr0.0200.024
    {gsm} - thresh: -2AE rate: flexible0.25197401400521216610.065tpr0.0610.068
    {gsm} - thresh: -2AE rate: flexible0.50164234726512216610.218tpr0.2130.224
    {gsm} - thresh: -2AE rate: flexible0.75123798772510216610.405tpr0.3980.412
    {gsm} - thresh: -2AE rate: flexible1.00883612331494216610.569tpr0.5630.576
    {simaerep} - thresh: 0.95AE rate: fix 0.001548019421382279 0156740.012216610.013 fpr 0.011 0.014
    {simaerep} - thresh: 0.95AE rate: fix 0.1014762912204421219 0156740.058216610.056 tpr0.0550.0620.0530.059
    {simaerep} - thresh: 0.95AE rate: fix 0.25122783396170794582 015674216610.212tpr0.206 0.217
    {simaerep} - thresh: 0.95AE rate: fix0.5010623110380216610.510tpr0.5030.516
    {simaerep} - thresh: 0.95AE rate: fix0.755895157660216610.728tpr0.7220.734
    {simaerep} - thresh: 0.95AE rate: fix1.004066175950216610.812tpr0.8070.817
    {simaerep} - thresh: 0.95AE rate: flexible0.00213912700216610.012fpr0.0110.014
    {simaerep} - thresh: 0.95AE rate: flexible0.102032113400216610.062tpr0.0590.065
    {simaerep} - thresh: 0.95AE rate: flexible0.251673049310216610.228 tpr0.2100.2230.2220.233
    {simaerep} - thresh: 0.95AE rate: flexible 0.50781578591005211609 0156740.501216610.536 tpr0.4940.5090.5290.543
    {simaerep} - thresh: 0.95AE rate: flexible 0.75462711047539416267 0156740.705216610.751 tpr0.6980.7120.7450.757
    {simaerep} - thresh: 0.95AE rate: flexible 1.00341612258373817923 0156740.782216610.827 tpr0.7750.7880.8220.832
    @@ -1306,9 +1578,12 @@

    Table

    Plot Performance Metrics

      -
    • {gsm} has better performance than {simaerep}
    • +
    • {gsm} has better performance than {simaerep} whent the AE rate is +fixed, while {simaerep} greatly outperforms {gsm} when the AE rate is +flexible and mimics the AE rates encountered in real study data +sets.
    -
    +
     plot_perf <- function(df_perf) {
     
       df_perf %>%
    @@ -1316,148 +1591,23 @@ 

    Plot Performance Metrics ur_rate = ifelse(str_detect(ur_rate, "fpr"), "fpr", ur_rate)) %>% group_by(ur_rate) %>% ggplot(aes(type, ratio)) + - geom_errorbar(aes(ymin = ci95_low, ymax = ci95_high), color = "violetred2") + - facet_wrap(~ ur_rate, ncol = 1) + + geom_errorbar(aes(ymin = ci95_low, ymax = ci95_high, color = type), linewidth = 1) + + facet_grid(ur_rate ~ ae_rate) + coord_flip() + + theme(legend.position = "bottom") + labs( x = "", y = "CI95 Performance Ratio", - title = "{simaerep} vs {gsm} Performance", - subtitle = "Only one under-reporting site per study." - ) + title = "{simaerep} vs {gsm} Performance" + ) + + scale_color_manual(values = c("#5491CC", "#F46626")) } plot_perf(df_perf)

    -

    -
    -
    -

    Analysis -

    -
      -
    • {gsm} correctly flags more sites in studies with <= 0.25 AE per -Visit with higher number of patients per site
    • -
    • {simarep} correctly flags more sites in studies with 0.25 - 0.5 AE -per visit with smaller number of patients per site
    • -
    • false positive flagging for {simaerep} is similarly distributed as -true positive flagging
    • -
    • false positive flagging for {gsm} evenly covers the whole -distribution range
    • -
    -
    -df_qu <- df_portf %>%
    -  summarize(n_pat = n_distinct(patnum), .by = c("study_id", "site_number", "ae_per_visit_mean")) %>%
    -  inner_join(
    -    df_sim %>%
    -      select(study_id, site_number = siteid, ur_rate, score)
    -  ) %>%
    -  inner_join(
    -    df_sim_simaerep %>%
    -      select(study_id, site_number, ur_rate, prob_low_prob_ur)
    -  ) %>%
    -  mutate(
    -    score = ifelse(is.na(score), 0 , score),
    -    flag = case_when(
    -      score <= -2 & prob_low_prob_ur >= 0.95 ~ "double",
    -      score <= -2 ~ "gsm only",
    -      prob_low_prob_ur >= 0.95 ~ "simaerep only",
    -      TRUE ~ "miss"),
    -    flag = fct_relevel(flag,"simaerep only", "gsm only", "double", "miss")
    -  )
    -
    -plot_flags <- function(df, ur_rate) {
    -  
    -  p <- df %>%
    -    filter(ur_rate == ur_rate) %>%
    -    ggplot(aes(log(n_pat), ae_per_visit_mean, colour = flag)) +
    -    geom_point(alpha = 0.25) +
    -    theme(legend.position = "bottom", aspect.ratio = 1) +
    -    labs(x = "Log Number of Patiends per Site", y = "AE per Visit Study Average", title = paste("Under Reporting Rate:", ur_rate))
    -  
    -  ggExtra::ggMarginal(p, groupColour = TRUE, groupFill = TRUE)
    -  
    -}
    -
    -p_p5 <- plot_flags(df_qu, 0.5)
    -
    -p_p75 <- plot_flags(df_qu, 0.75)
    -p_1 <- plot_flags(df_qu, 1)
    -
    -p_fpr <-  df_qu %>%
    -  filter(ur_rate == 0, flag != "miss") %>%
    -  ggplot(aes(log(n_pat), ae_per_visit_mean, colour = flag)) +
    -  geom_point(alpha = 0.25) +
    -    theme(legend.position = "bottom", aspect.ratio = 1) +
    -    labs(x = "Log Number of Patiends per Site", y = "AE per Visit Study Average", title = paste("False Positive Flags"))
    -
    -p_fpr <- ggExtra::ggMarginal(p_fpr, groupColour = TRUE, groupFill = TRUE)
    -
    -gridExtra::grid.arrange(p_p5, p_p75, p_1, p_fpr, ncol = 2)
    -

    -
    -
    -

    Combine both approaches -

    -
      -
    • clear performance increase only for studies with > 1 ae per visit -rate, which is very rare.
    • -
    -
    -df_comb1 <- df_qu %>%
    -  mutate(
    -    type = "comb. ae/visit thresh: 1",
    -    is_ur = (ae_per_visit_mean <= 1 & score <= -2) | (ae_per_visit_mean > 1 & prob_low_prob_ur >= 0.95)
    -  ) %>%
    -  select(type, study_id, site_number, ur_rate, is_ur)
    -
    -df_combp25 <- df_qu %>%
    -  mutate(
    -    type = "comb. ae/visit thresh: 0.25",
    -    is_ur = (ae_per_visit_mean <= 0.25 & score <= -2) | (ae_per_visit_mean > 0.25 & prob_low_prob_ur >= 0.95)
    -  ) %>%
    -  select(type, study_id, site_number, ur_rate, is_ur)
    -
    -df_combp5 <- df_qu %>%
    -  mutate(
    -    type = "comb. ae/visit thresh: 0.5",
    -    is_ur = (ae_per_visit_mean <= 0.5 & score <= -2) | (ae_per_visit_mean > 0.5 & prob_low_prob_ur >= 0.95)
    -  ) %>%
    -  select(type, study_id, site_number, ur_rate, is_ur)
    -
    -
    -df_comb <- bind_rows(
    -  df_eval,
    -  df_comb1,
    -  df_combp25,
    -  df_combp5
    -)
    -
    -df_comb_perf <- aggr_results(df_comb)
    -
    -plot_perf(df_comb_perf) +
    -  labs(title = "Combined Performance with Differect ")
    -

    -
    -
    -
    -

    Conclusion -

    -
      -
    • Across the portfolio {gsm} flagging performs better than {simaerep} -flagging.
    • -
    • A robust population of studies for which {simaerep} flagging is -preferable cannot be identified.
    • -
    -

    The simulation assumes a constant AE rate during the the study. This -is not true specifically during follow-up periods. By design {simaerep} -should be able to handle those cases better than {gsm}. A more complex -simulation is needed in order to proof this hypothesis.

    -

    Zink et -al. 2018 addressees the limitation of funnel plots for changing -event rates in different phases of the study and suggests to stratify -the data by relative time windows and present the results as a -heatmap.

    -
    +

    +
     plan(sequential)
    +
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plan(multisession, workers = 6) ``` @@ -36,60 +34,127 @@ devtools::install_github("Gilead-BioStats/gsm", ref = "main") ``` + # Introduction The [{gsm}](https://github.com/Gilead-BioStats/gsm/) R package provides a standardized Risk Based Quality Monitoring (RBQM) framework for clinical trials that pairs a flexible data pipeline with robust reports. It also uses Funnel Plots to flag outliers which provide broader tolerance limits for sites with low exposure and narrower limits for sites with higher exposure. This method is different to the event rate based limits we have used in previous heuristics to measure {simaerep} performance. Funnel plots are discussed in greater detail by [Zink et al. 2018](https://doi.org/10.1177/2168479017738981) +One of the draw backs of using funnel plots for flagging is that they assume that the AE rate remains constant over the course of the study. + # Prepare Data ## Load Portfolio Configurations + +We have prepared a snapshot of the AE reporting configuration of our current portfolio. For each study we have also measured a visit-specific AE rate which allows us to generate a synthetic portfolio with flexible AE rates across a study. + ```{r real_config} -df_config <- readr::read_csv("ae_profile.csv") +df_config <- readr::read_csv("ae_conf_20240220.csv") +df_ae_rates <- readr::read_csv("ae_rates_20240220.csv") df_config %>% head(25) %>% knitr::kable() + +df_ae_rates %>% + head(25) %>% + knitr::kable() + ``` ## Simulate Portfolio -And again we simulate artificial visit level data. Using parallel processing. At this stage we have simulated compliant test data of a realistic study portfolio. +We generate two synthetic portfolios with no AE under-reporting sites. One portfolio with a fixed AE rate for all visits and another one with a flexible visit-specific AE rate. ```{r sim_portf} -df_portf <- sim_test_data_portfolio(df_config, parallel = TRUE, progress = TRUE) +df_portf_fix <- sim_test_data_portfolio(df_config, parallel = TRUE, progress = TRUE) -df_portf %>% +df_portf_fix %>% head(25) %>% knitr::kable() + + +df_portf_flex <- sim_test_data_portfolio(df_config, df_ae_rates = df_ae_rates, parallel = TRUE, progress = TRUE) +``` + +## Compare AE rates + +Next we confirm the different AE rates in our two synthetic portfolios. + +```{r} +df_rate_fix <- df_portf_fix %>% + mutate(ae_rate = coalesce(n_ae - lag(n_ae), n_ae), .by = c("study_id", "patnum")) %>% + summarise(ae_rate = mean(ae_rate), .by = c("study_id", "visit")) %>% + mutate(rate = "fix") + + +df_rate_flex <- df_portf_flex %>% + mutate(ae_rate = coalesce(n_ae - lag(n_ae), n_ae), .by = c("study_id", "patnum")) %>% + summarise(ae_rate = mean(ae_rate), .by = c("study_id", "visit")) %>% + mutate(rate = "flex") +``` + +```{r fig.width=10, fig.height = 7} +bind_rows(df_rate_flex, df_rate_fix) %>% + ggplot(aes(visit, ae_rate)) + + geom_line(aes(group = study_id), alpha = 0.2) + + geom_smooth() + + facet_wrap(~ rate) + + labs(title = "Average AE rates per Study") +``` + + +```{r fig.width=10, fig.height = 7} +bind_rows(df_rate_flex, df_rate_fix) %>% + filter(dense_rank(study_id) <= 16) %>% + ggplot(aes(visit, ae_rate)) + + geom_line(aes(group = rate, color = rate)) + + facet_wrap(~ study_id, scales = "free") + + labs(title = "Average AE rates for Selected Studies") + ``` +We can confirm that the AE rates in the "flexible" portfolio are not constant. Moreover +we see that the AE rate profile is very unique for each study. + # Apply {gsm} ## Example +Here we demonstrate how to use the {gsm} package on our simulated portfolios. + ```{r} -dfSUBJ <- df_portf %>% - select(study_id, siteid = site_number, subjid = patnum, timeonstudy = visit) %>% - summarise(timeonstudy = max(timeonstudy), .by = c(study_id, siteid, subjid)) %>% - group_by(study_id) %>% - nest() - -dfAE <- df_portf %>% - select(study_id, subjid = patnum, n_ae) %>% - summarise(n_ae = max(n_ae), .by = c(study_id, subjid)) %>% - filter(n_ae > 0) %>% - mutate(n_ae = map(n_ae, ~ tibble(n = seq(1, .)), .progress = TRUE)) %>% - unnest(n_ae) %>% - select(- n) %>% - group_by(study_id) %>% - nest() + +get_SUBJ <- function(df_portf) { + df_portf %>% + select(study_id, siteid = site_number, subjid = patnum, timeonstudy = visit) %>% + summarise(timeonstudy = max(timeonstudy), .by = c(study_id, siteid, subjid)) %>% + group_by(study_id) %>% + nest() +} + + +get_AE <- function(df_portf) { + df_portf_fix %>% + select(study_id, subjid = patnum, n_ae) %>% + summarise(n_ae = max(n_ae), .by = c(study_id, subjid)) %>% + filter(n_ae > 0) %>% + mutate(n_ae = map(n_ae, ~ tibble(n = seq(1, .)), .progress = TRUE)) %>% + unnest(n_ae) %>% + select(- n) %>% + group_by(study_id) %>% + nest() +} + +dfSUBJ_fix <- get_SUBJ(df_portf_fix) +dfAE_fix <- get_AE(df_portf_fix) + ``` ```{r} -dfInput <- gsm::AE_Map_Raw(list(dfSUBJ = dfSUBJ$data[[1]], dfAE = dfAE$data[[1]])) +dfInput <- gsm::AE_Map_Raw(list(dfSUBJ = dfSUBJ_fix$data[[1]], dfAE = dfAE_fix$data[[1]])) dfInput dfTransformed <- gsm::Transform_Rate( @@ -117,8 +182,10 @@ chart ## Simulate UR +We write a function that removes a given ratio of AEs from one site in the data set and returns its z-score. + ```{r} -sim_gsm_ur <- function(site, ur_rate, dfTransformed) { +sim_site_ur_gsm <- function(site, ur_rate, dfTransformed) { dfTransformed <- dfTransformed %>% mutate( Numerator = ifelse(GroupID == site, Numerator * (1 - ur_rate), Numerator), @@ -130,36 +197,69 @@ sim_gsm_ur <- function(site, ur_rate, dfTransformed) { pull(Score) } -sim_gsm_ur("0001", ur_rate = 0.75, dfTransformed) +sim_site_ur_gsm("4747", ur_rate = 0.75, dfTransformed) ``` +We write another function that systematically applies this `sim_site_ur_gsm` across all sites in all studies across a range of under-reporting ratios. ```{r} -df_sim <- dfSUBJ %>% - inner_join(dfAE, by = "study_id") %>% - ungroup() %>% - mutate( - trans = map2(data.x, data.y, ~ gsm::AE_Map_Raw(list(dfSUBJ = .x, dfAE = .y))), - trans = map(trans, ~ gsm::Transform_Rate(., strNumeratorCol = "Count", strDenominatorCol = "Exposure")), - sites = map(data.x, ~ distinct(., siteid)) - ) %>% - select(- starts_with("data.")) %>% - unnest(sites) %>% - mutate(ur = list(tibble(ur_rate = c(0, 0.1, 0.25, 0.5, 0.75, 1)))) %>% - unnest(ur) %>% - mutate( - score = pmap_dbl(list(siteid, ur_rate, trans), sim_gsm_ur, .progress = TRUE) - ) - -df_sim +sim_ur_gsm <- function(dfSUBJ, dfAE) { + dfSUBJ %>% + inner_join(dfAE, by = "study_id") %>% + ungroup() %>% + mutate( + trans = map2(data.x, data.y, ~ gsm::AE_Map_Raw(list(dfSUBJ = .x, dfAE = .y))), + trans = map(trans, ~ gsm::Transform_Rate(., strNumeratorCol = "Count", strDenominatorCol = "Exposure")), + sites = map(data.x, ~ distinct(., siteid)) + ) %>% + select(- starts_with("data.")) %>% + unnest(sites) %>% + mutate(ur = list(tibble(ur_rate = c(0, 0.1, 0.25, 0.5, 0.75, 1)))) %>% + unnest(ur) %>% + mutate( + score = pmap_dbl(list(siteid, ur_rate, trans), sim_site_ur_gsm, .progress = TRUE) + ) +} + +df_sim_gsm_fix <- sim_ur_gsm(dfSUBJ_fix, dfAE_fix) +``` + +```{r} +df_sim_gsm_fix +``` + +We repeat the same steps for the portfolio with the flexible AE rates. + +```{r} + +dfSUBJ_flex <- get_SUBJ(df_portf_flex) +dfAE_flex<- get_AE(df_portf_flex) + + +df_sim_gsm_flex <- sim_ur_gsm(dfSUBJ_flex, dfAE_flex) ``` + # UR {simaerep} -```{r sim_ur_scen_kable} -df_sim_simaerep <- sim_ur_scenarios( - df_portf, +We simulate under-reporting for both portfolios using {simaerep} + +```{r} +df_sim_simaerep_fix <- sim_ur_scenarios( + df_portf_fix, + extra_ur_sites = 0, + ur_rate = c(0, 0.1, 0.25, 0.5, 0.75, 1), + parallel = TRUE, + poisson = TRUE, + prob_lower = TRUE, + progress = TRUE +) +``` + +```{r} +df_sim_simaerep_flex <- sim_ur_scenarios( + df_portf_flex, extra_ur_sites = 0, ur_rate = c(0, 0.1, 0.25, 0.5, 0.75, 1), parallel = TRUE, @@ -175,21 +275,26 @@ df_sim_simaerep <- sim_ur_scenarios( ```{r} -df_sim_thresh2 <- df_sim %>% +df_sim_gsm_fix$ae_rate <- "AE rate: fix" +df_sim_gsm_flex$ae_rate <- "AE rate: flexible" +df_sim_simaerep_fix$ae_rate <- "AE rate: fix" +df_sim_simaerep_flex$ae_rate <- "AE rate: flexible" + +df_sim_thresh2 <- bind_rows(df_sim_gsm_fix, df_sim_gsm_flex) %>% mutate( is_ur = score <= -2, type = "{gsm} - thresh: -2", site_number = siteid ) %>% - select(type, study_id, site_number, ur_rate, is_ur, score) + select(type, ae_rate, study_id, site_number, ur_rate, is_ur, score) -df_sim_simaerep_threshp95 <- df_sim_simaerep %>% +df_sim_simaerep_threshp95 <- bind_rows(df_sim_simaerep_fix, df_sim_simaerep_flex) %>% mutate( is_ur = prob_low_prob_ur >= 0.95, type = "{simaerep} - thresh: 0.95" ) %>% - select(type, study_id, site_number, ur_rate, is_ur, score = prob_low_prob_ur) + select(type, ae_rate, study_id, site_number, ur_rate, is_ur, score = prob_low_prob_ur) df_eval <- bind_rows( @@ -217,7 +322,7 @@ aggr_results <- function(df_eval) { df_perf <- df_eval %>% summarise( n = n(), - .by = c(type, ur_rate, is_ur) + .by = c(type, ae_rate, ur_rate, is_ur) ) %>% pivot_wider( names_from = is_ur, @@ -237,6 +342,8 @@ df_perf <- df_eval %>% df_perf <- aggr_results(df_eval) ``` +# Results + ## Table ```{r} @@ -249,9 +356,9 @@ df_perf %>% ## Plot Performance Metrics -- {gsm} has better performance than {simaerep} +- {gsm} has better performance than {simaerep} whent the AE rate is fixed, while {simaerep} greatly outperforms {gsm} when the AE rate is flexible and mimics the AE rates encountered in real study data sets. -```{r, plot, fig.width=6, fig.height = 10} +```{r, plot, fig.width=10, fig.height = 12} plot_perf <- function(df_perf) { @@ -260,132 +367,22 @@ plot_perf <- function(df_perf) { ur_rate = ifelse(str_detect(ur_rate, "fpr"), "fpr", ur_rate)) %>% group_by(ur_rate) %>% ggplot(aes(type, ratio)) + - geom_errorbar(aes(ymin = ci95_low, ymax = ci95_high), color = "violetred2") + - facet_wrap(~ ur_rate, ncol = 1) + + geom_errorbar(aes(ymin = ci95_low, ymax = ci95_high, color = type), linewidth = 1) + + facet_grid(ur_rate ~ ae_rate) + coord_flip() + + theme(legend.position = "bottom") + labs( x = "", y = "CI95 Performance Ratio", - title = "{simaerep} vs {gsm} Performance", - subtitle = "Only one under-reporting site per study." - ) + title = "{simaerep} vs {gsm} Performance" + ) + + scale_color_manual(values = c("#5491CC", "#F46626")) } plot_perf(df_perf) ``` -## Analysis - -- {gsm} correctly flags more sites in studies with <= 0.25 AE per Visit with higher number of patients per site -- {simarep} correctly flags more sites in studies with 0.25 - 0.5 AE per visit with smaller number of patients per site -- false positive flagging for {simaerep} is similarly distributed as true positive flagging -- false positive flagging for {gsm} evenly covers the whole distribution range - -```{r fig.width=10, fig.height = 10} - -df_qu <- df_portf %>% - summarize(n_pat = n_distinct(patnum), .by = c("study_id", "site_number", "ae_per_visit_mean")) %>% - inner_join( - df_sim %>% - select(study_id, site_number = siteid, ur_rate, score) - ) %>% - inner_join( - df_sim_simaerep %>% - select(study_id, site_number, ur_rate, prob_low_prob_ur) - ) %>% - mutate( - score = ifelse(is.na(score), 0 , score), - flag = case_when( - score <= -2 & prob_low_prob_ur >= 0.95 ~ "double", - score <= -2 ~ "gsm only", - prob_low_prob_ur >= 0.95 ~ "simaerep only", - TRUE ~ "miss"), - flag = fct_relevel(flag,"simaerep only", "gsm only", "double", "miss") - ) - -plot_flags <- function(df, ur_rate) { - - p <- df %>% - filter(ur_rate == ur_rate) %>% - ggplot(aes(log(n_pat), ae_per_visit_mean, colour = flag)) + - geom_point(alpha = 0.25) + - theme(legend.position = "bottom", aspect.ratio = 1) + - labs(x = "Log Number of Patiends per Site", y = "AE per Visit Study Average", title = paste("Under Reporting Rate:", ur_rate)) - - ggExtra::ggMarginal(p, groupColour = TRUE, groupFill = TRUE) - -} - -p_p5 <- plot_flags(df_qu, 0.5) - -p_p75 <- plot_flags(df_qu, 0.75) -p_1 <- plot_flags(df_qu, 1) - -p_fpr <- df_qu %>% - filter(ur_rate == 0, flag != "miss") %>% - ggplot(aes(log(n_pat), ae_per_visit_mean, colour = flag)) + - geom_point(alpha = 0.25) + - theme(legend.position = "bottom", aspect.ratio = 1) + - labs(x = "Log Number of Patiends per Site", y = "AE per Visit Study Average", title = paste("False Positive Flags")) - -p_fpr <- ggExtra::ggMarginal(p_fpr, groupColour = TRUE, groupFill = TRUE) - -gridExtra::grid.arrange(p_p5, p_p75, p_1, p_fpr, ncol = 2) -``` - -## Combine both approaches - -- clear performance increase only for studies with > 1 ae per visit rate, which is very rare. - -```{r, fig.width=6, fig.height = 10} - -df_comb1 <- df_qu %>% - mutate( - type = "comb. ae/visit thresh: 1", - is_ur = (ae_per_visit_mean <= 1 & score <= -2) | (ae_per_visit_mean > 1 & prob_low_prob_ur >= 0.95) - ) %>% - select(type, study_id, site_number, ur_rate, is_ur) - -df_combp25 <- df_qu %>% - mutate( - type = "comb. ae/visit thresh: 0.25", - is_ur = (ae_per_visit_mean <= 0.25 & score <= -2) | (ae_per_visit_mean > 0.25 & prob_low_prob_ur >= 0.95) - ) %>% - select(type, study_id, site_number, ur_rate, is_ur) - -df_combp5 <- df_qu %>% - mutate( - type = "comb. ae/visit thresh: 0.5", - is_ur = (ae_per_visit_mean <= 0.5 & score <= -2) | (ae_per_visit_mean > 0.5 & prob_low_prob_ur >= 0.95) - ) %>% - select(type, study_id, site_number, ur_rate, is_ur) - - -df_comb <- bind_rows( - df_eval, - df_comb1, - df_combp25, - df_combp5 -) - -df_comb_perf <- aggr_results(df_comb) - -plot_perf(df_comb_perf) + - labs(title = "Combined Performance with Differect ") -``` - - -# Conclusion - -- Across the portfolio {gsm} flagging performs better than {simaerep} flagging. -- A robust population of studies for which {simaerep} flagging is preferable cannot be identified. - -The simulation assumes a constant AE rate during the the study. This is not true specifically during follow-up periods. By design {simaerep} should be able to handle those cases better than {gsm}. A more complex simulation is needed in order to proof this hypothesis. - -[Zink et al. 2018](https://doi.org/10.1177/2168479017738981) addressees the limitation of funnel plots for changing event rates in different phases of the study and suggests to stratify the data by relative time windows and present the results as a heatmap. - - ```{r close} plan(sequential) From 0e5b75a29a9ea45990bf4605f957215fda61b841 Mon Sep 17 00:00:00 2001 From: Bjoern Koneswarakantha Date: Thu, 22 Feb 2024 14:20:05 +0100 Subject: [PATCH 4/7] fix dplyr warnings --- DESCRIPTION | 4 +- NAMESPACE | 2 +- NEWS.md | 5 + R/0_imports.R | 4 +- R/S3_orivisit.R | 2 +- R/simaerep.R | 163 +++--- R/simaerep_plot.R | 192 +++---- R/simulate_test_data.R | 63 ++- README.md | 5 - docs/404.html | 2 +- docs/LICENSE-text.html | 2 +- docs/LICENSE.html | 2 +- docs/articles/check_poisson.html | 110 ++-- .../figure-html/unnamed-chunk-4-1.png | Bin 80375 -> 78488 bytes .../figure-html/unnamed-chunk-5-1.png | Bin 76263 -> 74609 bytes 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docs/reference/eval_sites.html | 2 +- docs/reference/eval_sites_deprecated.html | 2 +- .../exp_implicit_missing_visits.html | 2 +- .../figures/README-unnamed-chunk-2-1.png | Bin 191174 -> 191425 bytes docs/reference/get_config.html | 2 +- docs/reference/get_ecd_values.html | 2 +- docs/reference/get_pat_pool_config.html | 2 +- docs/reference/get_portf_perf.html | 2 +- docs/reference/get_site_mean_ae_dev.html | 2 +- docs/reference/get_visit_med75.html | 2 +- docs/reference/index.html | 2 +- docs/reference/is_orivisit.html | 2 +- docs/reference/is_simaerep.html | 2 +- docs/reference/orivisit.html | 7 +- docs/reference/pat_aggr.html | 2 +- docs/reference/pat_pool.html | 2 +- docs/reference/pipe.html | 2 +- docs/reference/plot.simaerep.html | 2 +- docs/reference/plot_dots.html | 2 +- docs/reference/plot_sim_example.html | 2 +- docs/reference/plot_sim_examples.html | 2 +- docs/reference/plot_study.html | 2 +- docs/reference/plot_visit_med75.html | 2 +- .../poiss_test_site_ae_vs_study_ae.html | 2 +- docs/reference/prep_for_sim.html | 2 +- .../prob_lower_site_ae_vs_study_ae.html | 2 +- docs/reference/purrr_bar.html | 2 +- docs/reference/sim_after_prep.html | 2 +- docs/reference/sim_scenario.html | 2 +- docs/reference/sim_sites.html | 2 +- docs/reference/sim_studies.html | 2 +- docs/reference/sim_test_data_patient.html | 2 +- docs/reference/sim_test_data_portfolio.html | 13 +- docs/reference/sim_test_data_study.html | 26 +- docs/reference/sim_ur_scenarios.html | 106 ++-- docs/reference/simaerep.html | 2 +- docs/reference/site_aggr.html | 2 +- docs/reference/with_progress_cnd.html | 2 +- inst/WORDLIST | 5 +- man/figures/README-unnamed-chunk-2-1.png | Bin 191174 -> 191425 bytes man/sim_test_data_portfolio.Rd | 9 +- man/sim_test_data_study.Rd | 7 +- tests/testthat/_snaps/eval_sites.md | 4 +- vignettes/gsm_perf.Rmd | 2 +- 80 files changed, 884 insertions(+), 850 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 4ec6071..4969410 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: simaerep Title: Find Clinical Trial Sites Under-Reporting Adverse Events -Version: 0.4.3 +Version: 0.4.4 Authors@R: c( person(given = "Bjoern", family = "Koneswarakantha", @@ -44,6 +44,6 @@ Suggests: vdiffr, lintr Roxygen: list(markdown = TRUE) -RoxygenNote: 7.2.0 +RoxygenNote: 7.2.3 Language: en-US Config/testthat/edition: 3 diff --git a/NAMESPACE b/NAMESPACE index 4433ce6..12b80b4 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -46,7 +46,6 @@ importFrom(cowplot,get_legend) importFrom(cowplot,ggdraw) importFrom(cowplot,plot_grid) importFrom(dplyr,across) -importFrom(dplyr,all_equal) importFrom(dplyr,any_of) importFrom(dplyr,arrange) importFrom(dplyr,between) @@ -102,6 +101,7 @@ importFrom(purrr,possibly) importFrom(purrr,safely) importFrom(rlang,":=") importFrom(rlang,.data) +importFrom(rlang,.env) importFrom(rlang,enexpr) importFrom(rlang,env_has) importFrom(stats,ecdf) diff --git a/NEWS.md b/NEWS.md index 85249c9..19c05cf 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,8 @@ +# simaerep 0.4.4 +- allow flexible AE rates in data simulations +- add vignette comparing simaerep to gsm performance +- fix dplyr warnings + # simaerep 0.4.3 - delete performance unit tests (poisson faster than bootstrap) to accommodate CRAN request diff --git a/R/0_imports.R b/R/0_imports.R index af83d86..a07e7fa 100644 --- a/R/0_imports.R +++ b/R/0_imports.R @@ -19,13 +19,13 @@ if (getRversion() >= "2.15.1") { #' @importFrom furrr future_map future_pmap furrr_options #' @importFrom progressr with_progress #' @importFrom stringr str_count str_pad str_length -#' @importFrom rlang := .data enexpr env_has +#' @importFrom rlang := .data enexpr env_has .env #' @importFrom dplyr select mutate filter summarise group_by summarise_all summarise_at #' @importFrom dplyr mutate_all mutate_at ungroup vars bind_cols bind_rows pull #' @importFrom dplyr n_distinct distinct arrange right_join left_join inner_join #' @importFrom dplyr rename sample_n between row_number dense_rank desc case_when #' @importFrom dplyr group_by_at n is_grouped_df everything one_of lag any_of across -#' @importFrom dplyr lead all_equal +#' @importFrom dplyr lead #' @importFrom tidyr tibble unnest nest fill #' @importFrom knitr kable #' @importFrom tibble tibble diff --git a/R/S3_orivisit.R b/R/S3_orivisit.R index a92575f..9fb0b0b 100644 --- a/R/S3_orivisit.R +++ b/R/S3_orivisit.R @@ -120,7 +120,7 @@ as.data.frame.orivisit <- function(x, ..., env = parent.frame()) { dim <- dim(df) df_summary <- summarise_df_visit(df) - if (! all_equal(df_summary, x$df_summary)) stop.orivisit() + if (! all.equal(df_summary, x$df_summary, tolerance = 1e-4)) stop.orivisit() if (! all(dim == x$dim)) stop.orivisit() return(df) diff --git a/R/simaerep.R b/R/simaerep.R index 7159176..27e2381 100644 --- a/R/simaerep.R +++ b/R/simaerep.R @@ -43,13 +43,13 @@ check_df_visit <- function(df_visit) { cols_na <- df_visit %>% summarise_at( - vars( - .data$study_id, - .data$site_number, - .data$patnum, - .data$n_ae, - .data$visit - ), + vars(c( + "study_id", + "site_number", + "patnum", + "n_ae", + "visit" + )), anyNA ) %>% unlist() @@ -60,10 +60,10 @@ check_df_visit <- function(df_visit) { df_visit %>% summarise_at( - vars( - .data$n_ae, - .data$visit - ), + vars(c( + "n_ae", + "visit" + )), ~ is.numeric(.) ) %>% unlist() %>% @@ -129,13 +129,13 @@ exp_implicit_missing_visits <- function(df_visit) { group_by(.data$study_id) %>% mutate(min_study_visit = min(.data$visit), max_study_visit = max(.data$visit)) %>% - select( - .data$study_id, - .data$site_number, - .data$patnum, - .data$min_study_visit, - .data$max_study_visit - ) %>% + select(c( + "study_id", + "site_number", + "patnum", + "min_study_visit", + "max_study_visit" + )) %>% distinct() %>% mutate( visit = map2( @@ -144,14 +144,14 @@ exp_implicit_missing_visits <- function(df_visit) { function(x, y) seq(x, y, 1) ) ) %>% - unnest(.data$visit) %>% - select( - .data$study_id, - .data$site_number, - .data$patnum, - .data$min_study_visit, - .data$visit - ) + unnest("visit") %>% + select(c( + "study_id", + "site_number", + "patnum", + "min_study_visit", + "visit" + )) df_visit_out <- df_visit %>% group_by(.data$study_id, .data$site_number, .data$patnum) %>% @@ -164,7 +164,7 @@ exp_implicit_missing_visits <- function(df_visit) { ) %>% group_by(.data$study_id, .data$site_number, .data$patnum) %>% arrange(.data$visit) %>% - fill(.data$n_ae, .direction = "down") %>% + fill("n_ae", .direction = "down") %>% mutate( min_visit_pat = min(.data$min_visit_pat, na.rm = TRUE), max_visit_pat = max(.data$max_visit_pat, na.rm = TRUE) @@ -177,13 +177,13 @@ exp_implicit_missing_visits <- function(df_visit) { .data$visit <= .data$max_visit_pat ) %>% mutate(n_ae = ifelse(is.na(.data$n_ae), 0, .data$n_ae)) %>% - select( - .data$study_id, - .data$site_number, - .data$patnum, - .data$n_ae, - .data$visit - ) %>% + select(c( + "study_id", + "site_number", + "patnum", + "n_ae", + "visit" + )) %>% arrange(.data$study_id, .data$site_number, .data$patnum, .data$visit) if (nrow(df_visit_out) > nrow(df_visit)) { @@ -291,15 +291,15 @@ get_visit_med75 <- function(df_pat, .data$visit_med75 ) ) %>% - select(- .data$study_qup8_max_visit) + select(- "study_qup8_max_visit") } df_site <- df_site %>% - select(.data$study_id, - .data$site_number, - .data$n_pat, - .data$n_pat_with_med75, - .data$visit_med75) + select(c("study_id", + "site_number", + "n_pat", + "n_pat_with_med75", + "visit_med75")) return(df_site) } @@ -486,7 +486,7 @@ eval_sites_deprecated <- function(df_sim_sites, pval_p_vs_fp_ratio = ifelse(.data$pval_p_vs_fp_ratio < 1, 1, .data$pval_p_vs_fp_ratio), pval_prob_ur = 1 - 1 / .data$pval_p_vs_fp_ratio ) %>% - select(- .data$min_pval) + select(- "min_pval") } if ("prob_low" %in% names(df_out)) { @@ -575,20 +575,20 @@ get_ecd_values <- function(df_sim_studies, df_sim_sites, val_str) { df_ecd <- df_sim_studies %>% rename(val = !!as.symbol(val_str)) %>% - select(.data$study_id, .data$val) %>% - nest(data = c(.data$val)) %>% + select("study_id", "val") %>% + nest(data = "val") %>% mutate(.ecdf = map(.data$data, ~ possibly_ecdf(.$val))) %>% - select(- .data$data) + select(- "data") df_out <- df_sim_sites %>% rename(val = !!as.symbol(val_str)) %>% left_join(df_ecd, "study_id") %>% mutate(ecd_val = map2_dbl(.data$`.ecdf`, .data$val, apply_ecdf)) %>% rename( - !!as.symbol(val_str) := .data$val, - !!as.symbol(paste0(val_str, "_ecd")) := .data$ecd_val #nolint + !!as.symbol(val_str) := "val", + !!as.symbol(paste0(val_str, "_ecd")) := "ecd_val" #nolint ) %>% - select(- .data$`.ecdf`) + select(- ".ecdf") return(ungroup(df_out)) } @@ -628,11 +628,11 @@ pat_pool <- function(df_visit, df_site) { df_visit %>% left_join(df_site, by = c("study_id", "site_number")) %>% filter(.data$visit <= .data$max_visit_med75_study) %>% - select(.data$study_id, - .data$patnum, - .data$visit, - .data$n_ae) %>% - nest(pat_pool = c(.data$patnum, .data$visit, .data$n_ae)) + select(c("study_id", + "patnum", + "visit", + "n_ae")) %>% + nest(pat_pool = c("patnum", "visit", "n_ae")) } @@ -829,8 +829,7 @@ prep_for_sim <- function(df_site, df_visit) { n_ae_site = map(.data$n_ae_site, "n_ae"), n_ae_study = map(.data$n_ae_study, "n_ae") ) %>% - select(- .data$patients, - - .data$pat_pool) + select(- c("patients", "pat_pool")) return(df_sim_prep) @@ -908,15 +907,15 @@ sim_after_prep <- function(df_sim_prep, ~ ifelse(.data$n_pat_with_med75_study == 0, NA, .) ) ) %>% - select(- .data$n_ae_site, - .data$n_ae_study) %>% - select(.data$study_id, - .data$site_number, - .data$n_pat, - .data$n_pat_with_med75, - .data$visit_med75, - .data$mean_ae_site_med75, - .data$mean_ae_study_med75, - .data$n_pat_with_med75_study, + select(- c("n_ae_site", "n_ae_study")) %>% + select(c("study_id", + "site_number", + "n_pat", + "n_pat_with_med75", + "visit_med75", + "mean_ae_site_med75", + "mean_ae_study_med75", + "n_pat_with_med75_study"), dplyr::everything()) %>% ungroup() @@ -976,11 +975,11 @@ sim_after_prep <- function(df_sim_prep, get_pat_pool_config <- function(df_visit, df_site, min_n_pat_with_med75 = 1) { # site_config are the number of sites with their individual visit_med75 and n_pat_with_med75 - df_site_config <- select(df_site, - .data$study_id, - .data$site_number, - .data$visit_med75, - .data$n_pat_with_med75) %>% + df_site_config <- select(df_site,c( + "study_id", + "site_number", + "visit_med75", + "n_pat_with_med75")) %>% filter(.data$n_pat_with_med75 >= min_n_pat_with_med75) # pat_pool gives the patient pool for study from which we sample @@ -994,12 +993,12 @@ get_pat_pool_config <- function(df_visit, df_site, min_n_pat_with_med75 = 1) { pat_pool = map2(.data$pat_pool, .data$visit_med75, function(x, y) filter(x, visit == y)), n_pat_study = map2_dbl(.data$pat_pool, .data$n_pat_with_med75, function(x, y) nrow(x) - y) ) %>% - select(.data$study_id, - .data$site_number, - .data$visit_med75, - .data$n_pat_with_med75, - .data$n_pat_study, - .data$pat_pool) + select(c("study_id", + "site_number", + "visit_med75", + "n_pat_with_med75", + "n_pat_study", + "pat_pool")) return(ungroup(df_site_config)) } @@ -1113,7 +1112,7 @@ sim_studies <- function(df_visit, function(x, y) sample_n(x, y, replace = TRUE)), n_ae_study = map(.data$n_ae_study, "n_ae") ) %>% - select(- .data$pat_pool) + select(- "pat_pool") if (poisson_test) { df_config <- df_config %>% @@ -1132,10 +1131,10 @@ sim_studies <- function(df_visit, if (!keep_ae) { df_config <- df_config %>% - select(- .data$n_ae_site, - .data$n_ae_study) + select(- c("n_ae_site", "n_ae_study")) } else { df_config <- df_config %>% - mutate_at(vars(n_ae_site, n_ae_study), ~ map_chr(., paste, collapse = ",")) + mutate_at(vars(c("n_ae_site", "n_ae_study")), ~ map_chr(., paste, collapse = ",")) } return(ungroup(df_config)) @@ -1143,7 +1142,7 @@ sim_studies <- function(df_visit, df_sim <- tibble(r = seq.int(1, r, 1)) %>% mutate(n_ae_set = .f_map(.data$r, sim)) %>% - unnest(.data$n_ae_set) + unnest("n_ae_set") return(ungroup(df_sim)) } @@ -1220,10 +1219,10 @@ site_aggr <- function(df_visit, df_mean_ae_med75 <- df_mean_ae_dev %>% filter(.data$visit == .data$visit_med75) %>% - rename(mean_ae_site_med75 = .data$mean_ae_site) %>% - select(.data$study_id, - .data$site_number, - .data$mean_ae_site_med75) + rename(mean_ae_site_med75 = "mean_ae_site") %>% + select(c("study_id", + "site_number", + "mean_ae_site_med75")) # Add mean cumulative AE to site aggregate ---------------------- diff --git a/R/simaerep_plot.R b/R/simaerep_plot.R index 8f796ca..9c35e90 100644 --- a/R/simaerep_plot.R +++ b/R/simaerep_plot.R @@ -86,9 +86,9 @@ plot_dots <- function(df, } p <- df %>% - ggplot(aes_string("x", "y", color = "color")) + + ggplot(aes(x, y, color = .data$color)) + geom_point(size = size_dots) + - geom_text(aes_string(label = "n_ae"), + geom_text(aes(label = n_ae), color = "black" ) + geom_rect(aes( @@ -167,10 +167,10 @@ plot_sim_example <- function(substract_ae_per_pat = 0, site = LETTERS[1:3], patients = c(list(seq(1, 50, 1)), list(seq(1, 40, 1)), list(seq(1, 10, 1))) ) %>% - tidyr::unnest(.data$patients) %>% + tidyr::unnest("patients") %>% mutate(n_ae = as.integer(runif(min = 0, max = 10, n = nrow(.)))) %>% mutate( - n_ae = ifelse(.data$site == "C", .data$n_ae - substract_ae_per_pat, .data$n_ae), + n_ae = ifelse(.data$site == "C", .data$n_ae - .env$substract_ae_per_pat, .data$n_ae), n_ae = ifelse(.data$n_ae < 0, 0, .data$n_ae) ) @@ -226,7 +226,7 @@ plot_sim_example <- function(substract_ae_per_pat = 0, group_by(.data$x) %>% mutate(y = row_number()) %>% filter(.data$rep <= 1000) %>% - ggplot(aes_string("x", "y", fill = "color")) + + ggplot(aes(x, y, fill = .data$color)) + geom_raster() + scale_fill_identity() + theme_minimal() + @@ -331,7 +331,7 @@ plot_sim_examples <- function(substract_ae_per_pat = c(0, 1, 3), ...) { title_add = map(.data$title_add, make_title, angle = 90), p = pmap( list( - substract_ae_per_pat = .data$substract_ae_per_pat, + substract_ae_per_pat = substract_ae_per_pat, title = .data$title, legend = .data$legend ), @@ -348,7 +348,7 @@ plot_sim_examples <- function(substract_ae_per_pat = c(0, 1, 3), ...) { site = LETTERS[1:3], patients = c(list(seq(1, 50, 1)), list(seq(1, 40, 1)), list(seq(1, 10, 1))) ) %>% - tidyr::unnest(.data$patients) %>% + tidyr::unnest("patients") %>% mutate(n_ae = as.integer(runif(min = 0, max = 10, n = nrow(.)))) p_legend <- plot_dots(study) + @@ -445,11 +445,11 @@ plot_study <- function(df_visit, alert_level_study = NA) } else { df_visit <- df_visit %>% - left_join(select(df_al, - .data$study_id, - .data$site_number, - .data$alert_level_site, - .data$alert_level_study)) + left_join(select(df_al, c( + "study_id", + "site_number", + "alert_level_site", + "alert_level_study"))) } # fill in pvalues when missing -------------------------------------------- @@ -462,13 +462,13 @@ plot_study <- function(df_visit, # make sure ids are character columns df_visit <- df_visit %>% - mutate_at(vars(.data$study_id, .data$patnum, .data$site_number), as.character) + mutate_at(vars(c("study_id", "patnum", "site_number")), as.character) df_eval <- df_eval %>% - mutate_at(vars(.data$study_id, .data$site_number), as.character) + mutate_at(vars(c("study_id", "site_number")), as.character) df_site <- df_site %>% - mutate_at(vars(.data$study_id, .data$site_number), as.character) + mutate_at(vars(c("study_id", "site_number")), as.character) # filter studies ----------------------------------------------------------- @@ -539,11 +539,11 @@ plot_study <- function(df_visit, ungroup() df_ae_dev_patient <- df_visit %>% - select(.data$study_id, - .data$site_number, - .data$patnum, - .data$visit, - .data$n_ae) %>% + select(c("study_id", + "site_number", + "patnum", + "visit", + "n_ae")) %>% mutate(max_visit_per_pat = max(.data$visit)) %>% filter(.data$site_number %in% sites_ordered) %>% ungroup() %>% @@ -566,7 +566,7 @@ plot_study <- function(df_visit, ) df_mean_ae_dev_site <- df_mean_ae_dev_site %>% - select(- .data$visit_med75) %>% + select(- "visit_med75") %>% left_join(df_eval, by = c("study_id", "site_number")) # we have to split site ae dev up because alert sites get plotted @@ -579,45 +579,45 @@ plot_study <- function(df_visit, filter(.data$prob_cut != levels(.data$prob_cut)[1]) df_alert <- df_visit %>% - select(.data$study_id, - .data$site_number, - .data$alert_level_site, - .data$alert_level_study) %>% + select(c("study_id", + "site_number", + "alert_level_site", + "alert_level_study")) %>% distinct() df_label <- df_mean_ae_dev_site %>% filter(.data$visit == .data$visit_med75, .data$site_number %in% sites_ordered) %>% - select(.data$study_id, - .data$site_number, - .data$visit, - .data$mean_ae) %>% + select(c("study_id", + "site_number", + "visit", + "mean_ae")) %>% left_join(df_site, by = c("study_id", "site_number")) %>% - select(.data$study_id, - .data$site_number, - .data$visit, - .data$mean_ae, - .data$n_pat, - .data$n_pat_with_med75) %>% + select(c("study_id", + "site_number", + "visit", + "mean_ae", + "n_pat", + "n_pat_with_med75")) %>% left_join( - select(df_eval, - - .data$n_pat, - - .data$n_pat_with_med75) + select(df_eval, - c( + "n_pat", + "n_pat_with_med75")) , by = c("study_id", "site_number") ) %>% left_join(df_alert, by = c("study_id", "site_number")) %>% - select( - .data$study_id, - .data$site_number, - .data$visit, - .data$mean_ae, - .data$n_pat, - .data$n_pat_with_med75, - .data$prob_low_prob_ur, - .data$prob_cut, - .data$color_prob_cut, - .data$pval_prob_ur, - .data$alert_level_site - ) %>% + select(c( + "study_id", + "site_number", + "visit", + "mean_ae", + "n_pat", + "n_pat_with_med75", + "prob_low_prob_ur", + "prob_cut", + "color_prob_cut", + "pval_prob_ur", + "alert_level_site" + )) %>% mutate( site_number = fct_relevel(.data$site_number, sites_ordered), color_alert_level = case_when( @@ -643,22 +643,22 @@ plot_study <- function(df_visit, ) p_study <- df_mean_ae_dev_site_no_alert %>% - ggplot(aes_string("visit", "mean_ae"), na.rm = TRUE) + - geom_line(aes_string( - group = "site_number", - color = "color_prob_cut" + ggplot(aes(visit, mean_ae), na.rm = TRUE) + + geom_line(aes( + group = .data$site_number, + color = color_prob_cut )) + - geom_line(aes_string( - group = "site_number", - color = "color_prob_cut" - ), - data = df_mean_ae_dev_site_alert, - size = 1 + geom_line(aes( + group = .data$site_number, + color = color_prob_cut + ), + data = df_mean_ae_dev_site_alert, + linewidth = 1 ) + - geom_line(aes_string(group = "study_id"), + geom_line(aes(group = .data$study_id), data = df_mean_ae_dev_study, color = "gold3", - size = 1, + linewidth = 1, alpha = 0.5 ) + geom_point( @@ -704,23 +704,23 @@ plot_study <- function(df_visit, ) p_site <- df_ae_dev_patient %>% - ggplot(aes_string("visit", "n_ae"), na.rm = TRUE) + - geom_line(aes_string(group = "patnum"), + ggplot(aes(visit, n_ae), na.rm = TRUE) + + geom_line(aes(group = patnum), color = "grey", alpha = 0.5 ) + - geom_line(aes_string( - y = "mean_ae", - color = "color_prob_cut", - alpha = 0.5 - ), - data = df_mean_ae_dev_site, - size = 1 + geom_line(aes( + y = mean_ae, + color = color_prob_cut, + alpha = 0.5 + ), + data = df_mean_ae_dev_site, + linewidth = 1 ) + geom_line(aes(y = mean_ae), data = df_mean_ae_dev_study, color = "gold3", - size = 1, + linewidth = 1, alpha = 0.5) + geom_text(aes(label = paste0(n_pat_with_med75, "/", n_pat)), data = df_label, @@ -733,8 +733,8 @@ plot_study <- function(df_visit, x = 0.8 * max_visit, y = 0.9 * max_ae, na.rm = TRUE) + - geom_label(aes_string(label = "alert_level_site", - color = "color_alert_level"), + geom_label(aes(label = .data$alert_level_site, + color = .data$color_alert_level), data = df_label, x = 0.5 * max_visit, y = 0.9 * max_ae, @@ -827,14 +827,14 @@ plot_visit_med75 <- function(df_visit, df_mean_ae_dev <- get_site_mean_ae_dev(df_visit, df_pat, df_site_max_med75) df_plot <- df_site_min_med75 %>% - rename(visit_med75_min = .data$visit_med75) %>% + rename(visit_med75_min = "visit_med75") %>% left_join( select( - df_site_max_med75, - .data$study_id, - .data$site_number, - visit_med75_max = .data$visit_med75 - ), + df_site_max_med75, c( + "study_id", + "site_number", + visit_med75_max = "visit_med75" + )), by = c( "study_id", "site_number" @@ -843,7 +843,7 @@ plot_visit_med75 <- function(df_visit, mutate(rnk_sites = rank(desc(.data$n_pat), ties.method = "first")) %>% filter(.data$rnk_sites <= n_sites) %>% left_join(df_visit, by = c("study_id", "site_number")) %>% - left_join(select(df_mean_ae_dev, - .data$visit_med75), + left_join(select(df_mean_ae_dev, - "visit_med75"), by = c("study_id", "site_number", "visit") ) %>% group_by(.data$study_id, .data$site_number, .data$patnum) %>% @@ -854,12 +854,12 @@ plot_visit_med75 <- function(df_visit, ungroup() df_label <- df_plot %>% - select( - .data$study_id, - .data$site_number, - .data$n_pat, - .data$n_pat_with_med75 - ) %>% + select(c( + "study_id", + "site_number", + "n_pat", + "n_pat_with_med75" + )) %>% distinct() %>% mutate(label = paste0(.data$n_pat_with_med75, "/", .data$n_pat)) @@ -875,30 +875,30 @@ plot_visit_med75 <- function(df_visit, filter(.data$site_number %in% unique(df_plot$site_number)) p <- df_plot %>% - ggplot(aes_string("visit", "n_ae")) + + ggplot(aes(visit, n_ae)) + geom_line( - aes_string(group = "patnum"), + aes(group = patnum), data = filter(df_plot, .data$has_med75 == "yes"), color = "grey" ) + geom_line( - aes_string(y = "mean_ae_site", group = "site_number"), + aes(y = .data$mean_ae_site, group = .data$site_number), data = df_mean_ae_dev, color = "slateblue3", alpha = 0.5, - size = 2 + linewidth = 2 ) + - geom_line(aes_string(group = "patnum"), + geom_line(aes(group = patnum), filter(df_plot, .data$has_med75 == "no"), color = "black", linetype = 2 ) + - geom_vline(aes_string(xintercept = "visit_med75_max"), + geom_vline(aes(xintercept = .data$visit_med75_max), linetype = 1, color = "slateblue3" ) + geom_vline( - aes_string(xintercept = "visit_med75_min"), + aes(xintercept = .data$visit_med75_min), linetype = 3, color = "slateblue3" ) + @@ -907,7 +907,7 @@ plot_visit_med75 <- function(df_visit, linetype = 2, color = "slateblue3" ) + - geom_text(aes_string(label = "label"), + geom_text(aes(label = label), df_label, x = 0.15 * visit_max, y = 0.75 * ae_max, diff --git a/R/simulate_test_data.R b/R/simulate_test_data.R index 4ddd6eb..9a23077 100644 --- a/R/simulate_test_data.R +++ b/R/simulate_test_data.R @@ -91,9 +91,9 @@ sim_test_data_study <- function(n_pat = 1000, ) tibble( + ae_per_visit_mean = ae_per_visit_mean, visit = seq(1, length(aes)), n_ae = aes, - ae_per_visit_mean = ae_per_visit_mean ) } @@ -111,13 +111,13 @@ sim_test_data_study <- function(n_pat = 1000, site_number = paste0("S", .data$site_number), max_visit_mean = max_visit_mean, max_visit_sd = max_visit_sd, - aes = pmap(list(max_visit_mean, - max_visit_sd, - is_ur), + aes = pmap(list(.data$max_visit_mean, + .data$max_visit_sd, + .data$is_ur), f_sim_pat ) ) %>% - unnest(aes) + unnest("aes") } @@ -344,26 +344,26 @@ sim_ur_scenarios <- function(df_portf, ur_rate <- ur_rate[ur_rate > 0] df_grid_gr0 <- df_site %>% - select(.data$study_id, .data$site_number, .data$n_pat_with_med75, .data$visit_med75) %>% + select(c("study_id", "site_number", "n_pat_with_med75", "visit_med75")) %>% left_join(df_mean_pat, by = c(study_id = "study_id", visit_med75 = "visit")) %>% mutate(extra_ur_sites = list(0:extra_ur_sites)) %>% - unnest(.data$extra_ur_sites) %>% + unnest("extra_ur_sites") %>% mutate( frac_pat_with_ur = (.data$n_pat_with_med75 + .data$extra_ur_sites * .data$mean_n_pat) / .data$sum_n_pat, ur_rate = list(ur_rate) ) %>% - unnest(.data$ur_rate) %>% - select( - .data$study_id, - .data$site_number, - .data$extra_ur_sites, - .data$frac_pat_with_ur, - .data$ur_rate - ) + unnest("ur_rate") %>% + select(c( + "study_id", + "site_number", + "extra_ur_sites", + "frac_pat_with_ur", + "ur_rate" + )) df_grid_0 <- df_grid_gr0 %>% - select(.data$study_id, .data$site_number) %>% + select(c("study_id", "site_number")) %>% distinct() %>% mutate(extra_ur_sites = 0, frac_pat_with_ur = 0, @@ -383,10 +383,10 @@ sim_ur_scenarios <- function(df_portf, sim_scenario ) ) %>% - select(- .data$n_ae_site, - .data$n_ae_study) %>% + select(- c("n_ae_site", "n_ae_study")) %>% mutate(n_ae_site = map(.data$scenarios, "n_ae_site"), n_ae_study = map(.data$scenarios, "n_ae_study")) %>% - select(- .data$scenarios) + select(- "scenarios") if (progress) { message("getting under-reporting stats") @@ -406,7 +406,7 @@ sim_ur_scenarios <- function(df_portf, group_by(.data$study_id_gr, .data$site_number_gr) %>% nest() %>% ungroup() %>% - select(- .data$study_id_gr, - .data$site_number_gr) + select(- c("study_id_gr", "site_number_gr")) with_progress_cnd( ls_df_sim_sites <- purrr_bar( @@ -545,13 +545,13 @@ sim_test_data_portfolio <- function(df_config, df_ae_rates = NULL, parallel = FA df_config$ae_rates <- NA } else { df_ae_rates <- df_ae_rates %>% - select(study_id, ae_rate) %>% - group_by(study_id) %>% + select(c("study_id", "ae_rate")) %>% + group_by(.data$study_id) %>% nest() %>% mutate( - ae_rates = map(data, "ae_rate") + ae_rates = map(.data$data, "ae_rate") ) %>% - select(- data) + select(- "data") df_config <- df_config %>% inner_join(df_ae_rates, by = "study_id") @@ -592,10 +592,9 @@ sim_test_data_portfolio <- function(df_config, df_ae_rates = NULL, parallel = FA ae_per_visit_mean = ae_per_visit_mean, ae_rates = ae_rates ) %>% - select( - # using rlang::.data here causes error with furrr - patnum, visit, n_ae - ) + select(c( + "patnum", "visit", "n_ae" + )) }, .progress = progress, .purrr_args = .purrr_args, @@ -606,7 +605,7 @@ sim_test_data_portfolio <- function(df_config, df_ae_rates = NULL, parallel = FA ) df_portf <- df_config_sim %>% - unnest(.data$sim) %>% + unnest("sim") %>% select(- c("n_pat" ,"ae_rates")) %>% group_by(.data$study_id) %>% mutate( @@ -694,7 +693,7 @@ get_config <- function(df_site, pad_width = 4) { stopifnot(c("study_id", "site_number", "patnum", "max_visit", "max_ae") %in% colnames(df_site)) - stopifnot(nrow(df_site) == nrow(distinct(select(df_site, .data$study_id, .data$site_number, .data$patnum)))) + stopifnot(nrow(df_site) == nrow(distinct(select(df_site, c("study_id", "site_number", "patnum"))))) df_site %>% summarise_all(~ ! anyNA(.)) %>% @@ -816,7 +815,7 @@ get_portf_perf <- function(df_scen, stat = "prob_low_prob_ur", fpr = c(0.001, 0. 0, .data$n_sites_total) ) %>% - select(.data$extra_ur_sites, .data$ur_rate, .data$ratio_sites_with_na) %>% + select(c("extra_ur_sites", "ur_rate", "ratio_sites_with_na")) %>% knitr::kable() %>% paste(collapse = "\n") @@ -842,7 +841,7 @@ get_portf_perf <- function(df_scen, stat = "prob_low_prob_ur", fpr = c(0.001, 0. df_prep <- df_scen %>% mutate(data = list(df_thresh)) %>% - unnest(.data$data) %>% + unnest("data") %>% mutate(stat = .data[[stat]]) %>% group_by(.data$fpr, .data$thresh, .data$extra_ur_sites, .data$ur_rate) %>% summarise( @@ -853,7 +852,7 @@ get_portf_perf <- function(df_scen, stat = "prob_low_prob_ur", fpr = c(0.001, 0. df_prep_0 <- df_prep %>% filter(.data$ur_rate == 0) %>% mutate(extra_ur_sites = list(unique(df_prep$extra_ur_sites))) %>% - unnest(.data$extra_ur_sites) + unnest("extra_ur_sites") df_prep_gr0 <- df_prep %>% filter(.data$ur_rate > 0) diff --git a/README.md b/README.md index 88f8d32..d3a2eed 100644 --- a/README.md +++ b/README.md @@ -151,11 +151,6 @@ df_visit %>% aerep <- simaerep(df_visit) plot(aerep, study = "A") -#> Warning: `all_equal()` was deprecated in dplyr 1.1.0. -#> ℹ Please use `all.equal()` instead. -#> ℹ And manually order the rows/cols as needed -#> ℹ The deprecated feature was likely used in the simaerep package. -#> Please report the issue to the authors. ``` diff --git a/docs/404.html b/docs/404.html index 698d87f..f31c8d5 100644 --- a/docs/404.html +++ b/docs/404.html @@ -45,7 +45,7 @@ simaerep - 0.4.3 + 0.4.4
    diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index 0394b8d..34fc4a9 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4
    diff --git a/docs/LICENSE.html b/docs/LICENSE.html index 1d4c349..806fcf5 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4

    diff --git a/docs/articles/check_poisson.html b/docs/articles/check_poisson.html index 35d5db9..7c8042d 100644 --- a/docs/articles/check_poisson.html +++ b/docs/articles/check_poisson.html @@ -46,7 +46,7 @@ simaerep - 0.4.3 + 0.4.4
    @@ -201,7 +201,7 @@

    Test Data and Standard Processingdf_sim_sites <- sim_sites(df_site, df_visit) df_visit

    -
    ## # A tibble: 58,187 × 9
    +
    ## # A tibble: 58,367 × 9
     ##    patnum  site_number is_ur max_visit_mean max_visit_sd ae_per_visit_mean visit
     ##    <chr>   <chr>       <lgl>          <dbl>        <dbl>             <dbl> <int>
     ##  1 P000001 S0001       TRUE              20            4              0.25     1
    @@ -214,39 +214,39 @@ 

    Test Data and Standard Processing## 8 P000001 S0001 TRUE 20 4 0.25 8 ## 9 P000001 S0001 TRUE 20 4 0.25 9 ## 10 P000001 S0001 TRUE 20 4 0.25 10 -## # ℹ 58,177 more rows +## # ℹ 58,357 more rows ## # ℹ 2 more variables: n_ae <int>, study_id <chr>

     df_site
    ## # A tibble: 300 × 6
     ##    study_id    site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75
     ##    <chr>       <chr>       <int>            <int>       <dbl>              <dbl>
    -##  1 ae_per_vis… S0001          10                9          17              0.333
    -##  2 ae_per_vis… S0002          10                9          17              1    
    -##  3 ae_per_vis… S0003          10                7          19              0.429
    -##  4 ae_per_vis… S0004          10                8          18              0.375
    -##  5 ae_per_vis… S0005          10                8          17              0.375
    -##  6 ae_per_vis… S0006          10                9          16              0.222
    -##  7 ae_per_vis… S0007          10                9          16              0.778
    -##  8 ae_per_vis… S0008          10               10          14              0.7  
    -##  9 ae_per_vis… S0009          10                9          16              0.333
    -## 10 ae_per_vis… S0010          10                9          15              0.333
    +##  1 ae_per_vis… S0001          10                9          16              0.556
    +##  2 ae_per_vis… S0002          10               10          14              0.3  
    +##  3 ae_per_vis… S0003          10                8          15              0.375
    +##  4 ae_per_vis… S0004          10                8          14              0.375
    +##  5 ae_per_vis… S0005          10                9          17              0.333
    +##  6 ae_per_vis… S0006          10                7          15              0.143
    +##  7 ae_per_vis… S0007          10                8          16              0.375
    +##  8 ae_per_vis… S0008          10                7          19              0.714
    +##  9 ae_per_vis… S0009          10                9          15              0.333
    +## 10 ae_per_vis… S0010          10                7          15              0.714
     ## # ℹ 290 more rows
     df_sim_sites
    ## # A tibble: 300 × 10
     ##    study_id    site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75
     ##    <chr>       <chr>       <int>            <int>       <dbl>              <dbl>
    -##  1 ae_per_vis… S0001          10                9          17              0.333
    -##  2 ae_per_vis… S0002          10                9          17              1    
    -##  3 ae_per_vis… S0003          10                7          19              0.429
    -##  4 ae_per_vis… S0004          10                8          18              0.375
    -##  5 ae_per_vis… S0005          10                8          17              0.375
    -##  6 ae_per_vis… S0006          10                9          16              0.222
    -##  7 ae_per_vis… S0007          10                9          16              0.778
    -##  8 ae_per_vis… S0008          10               10          14              0.7  
    -##  9 ae_per_vis… S0009          10                9          16              0.333
    -## 10 ae_per_vis… S0010          10                9          15              0.333
    +##  1 ae_per_vis… S0001          10                9          16              0.556
    +##  2 ae_per_vis… S0002          10               10          14              0.3  
    +##  3 ae_per_vis… S0003          10                8          15              0.375
    +##  4 ae_per_vis… S0004          10                8          14              0.375
    +##  5 ae_per_vis… S0005          10                9          17              0.333
    +##  6 ae_per_vis… S0006          10                7          15              0.143
    +##  7 ae_per_vis… S0007          10                8          16              0.375
    +##  8 ae_per_vis… S0008          10                7          19              0.714
    +##  9 ae_per_vis… S0009          10                9          15              0.333
    +## 10 ae_per_vis… S0010          10                7          15              0.714
     ## # ℹ 290 more rows
     ## # ℹ 4 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>,
     ## #   pval <dbl>, prob_low <dbl>
    @@ -271,18 +271,18 @@

    Simulate Studies df_sim_studies

    ## # A tibble: 30,000 × 8
    -##        r study_id     site_number visit_med75 n_pat_with_med75 n_pat_study  pval
    -##    <dbl> <chr>        <chr>             <dbl>            <int>       <dbl> <dbl>
    -##  1     1 ae_per_visi… S0001                17                9         738 1    
    -##  2     1 ae_per_visi… S0002                17                9         738 1    
    -##  3     1 ae_per_visi… S0003                19                7         571 0.305
    -##  4     1 ae_per_visi… S0004                18                8         663 1    
    -##  5     1 ae_per_visi… S0005                17                8         739 1    
    -##  6     1 ae_per_visi… S0006                16                9         820 0.571
    -##  7     1 ae_per_visi… S0007                16                9         820 0.570
    -##  8     1 ae_per_visi… S0008                14               10         918 1    
    -##  9     1 ae_per_visi… S0009                16                9         820 0.236
    -## 10     1 ae_per_visi… S0010                15                9         871 0.839
    +##        r study_id    site_number visit_med75 n_pat_with_med75 n_pat_study   pval
    +##    <dbl> <chr>       <chr>             <dbl>            <int>       <dbl>  <dbl>
    +##  1     1 ae_per_vis… S0001                16                9         819 1     
    +##  2     1 ae_per_vis… S0002                14               10         911 1     
    +##  3     1 ae_per_vis… S0003                15                8         872 0.0511
    +##  4     1 ae_per_vis… S0004                14                8         913 0.827 
    +##  5     1 ae_per_vis… S0005                17                9         750 1     
    +##  6     1 ae_per_vis… S0006                15                7         873 1     
    +##  7     1 ae_per_vis… S0007                16                8         820 0.391 
    +##  8     1 ae_per_vis… S0008                19                7         564 1     
    +##  9     1 ae_per_vis… S0009                15                9         871 1     
    +## 10     1 ae_per_vis… S0010                15                7         873 1     
     ## # ℹ 29,990 more rows
     ## # ℹ 1 more variable: prob_low <dbl>
    @@ -304,16 +304,16 @@

    Check p-value Probabilities## # A tibble: 300 × 11 ## study_id site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75 ## <chr> <chr> <int> <int> <dbl> <dbl> -## 1 ae_per_vis… S0001 10 9 17 0.333 -## 2 ae_per_vis… S0002 10 9 17 1 -## 3 ae_per_vis… S0003 10 7 19 0.429 -## 4 ae_per_vis… S0004 10 8 18 0.375 -## 5 ae_per_vis… S0005 10 8 17 0.375 -## 6 ae_per_vis… S0006 10 9 16 0.222 -## 7 ae_per_vis… S0007 10 9 16 0.778 -## 8 ae_per_vis… S0008 10 10 14 0.7 -## 9 ae_per_vis… S0009 10 9 16 0.333 -## 10 ae_per_vis… S0010 10 9 15 0.333 +## 1 ae_per_vis… S0001 10 9 16 0.556 +## 2 ae_per_vis… S0002 10 10 14 0.3 +## 3 ae_per_vis… S0003 10 8 15 0.375 +## 4 ae_per_vis… S0004 10 8 14 0.375 +## 5 ae_per_vis… S0005 10 9 17 0.333 +## 6 ae_per_vis… S0006 10 7 15 0.143 +## 7 ae_per_vis… S0007 10 8 16 0.375 +## 8 ae_per_vis… S0008 10 7 19 0.714 +## 9 ae_per_vis… S0009 10 9 15 0.333 +## 10 ae_per_vis… S0010 10 7 15 0.714 ## # ℹ 290 more rows ## # ℹ 5 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>, ## # pval <dbl>, prob_low <dbl>, pval_ecd <dbl>

    @@ -343,16 +343,16 @@

    Perform Same Check on
    ## # A tibble: 300 × 11
     ##    study_id    site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75
     ##    <chr>       <chr>       <int>            <int>       <dbl>              <dbl>
    -##  1 ae_per_vis… S0001          10                9          17              0.333
    -##  2 ae_per_vis… S0002          10                9          17              1    
    -##  3 ae_per_vis… S0003          10                7          19              0.429
    -##  4 ae_per_vis… S0004          10                8          18              0.375
    -##  5 ae_per_vis… S0005          10                8          17              0.375
    -##  6 ae_per_vis… S0006          10                9          16              0.222
    -##  7 ae_per_vis… S0007          10                9          16              0.778
    -##  8 ae_per_vis… S0008          10               10          14              0.7  
    -##  9 ae_per_vis… S0009          10                9          16              0.333
    -## 10 ae_per_vis… S0010          10                9          15              0.333
    +##  1 ae_per_vis… S0001          10                9          16              0.556
    +##  2 ae_per_vis… S0002          10               10          14              0.3  
    +##  3 ae_per_vis… S0003          10                8          15              0.375
    +##  4 ae_per_vis… S0004          10                8          14              0.375
    +##  5 ae_per_vis… S0005          10                9          17              0.333
    +##  6 ae_per_vis… S0006          10                7          15              0.143
    +##  7 ae_per_vis… S0007          10                8          16              0.375
    +##  8 ae_per_vis… S0008          10                7          19              0.714
    +##  9 ae_per_vis… S0009          10                9          15              0.333
    +## 10 ae_per_vis… S0010          10                7          15              0.714
     ## # ℹ 290 more rows
     ## # ℹ 5 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>,
     ## #   pval <dbl>, prob_low <dbl>, prob_low_ecd <dbl>
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      Simulate Portfolio16.5 0001 2 -1 +0 0001 @@ -630,7 +630,7 @@

      Simulate Portfolio16.5 0001 3 -1 +0 0001 @@ -640,7 +640,7 @@

      Simulate Portfolio16.5 0001 4 -1 +0 0001 @@ -650,7 +650,7 @@

      Simulate Portfolio16.5 0001 5 -1 +0 0001 @@ -660,7 +660,7 @@

      Simulate Portfolio16.5 0001 6 -1 +0 0001 @@ -670,7 +670,7 @@

      Simulate Portfolio16.5 0001 7 -2 +0 0001 @@ -680,7 +680,7 @@

      Simulate Portfolio16.5 0001 8 -2 +0 0001 @@ -690,7 +690,7 @@

      Simulate Portfolio16.5 0001 9 -2 +0 0001 @@ -700,7 +700,7 @@

      Simulate Portfolio16.5 0001 10 -2 +0 0001 @@ -710,7 +710,7 @@

      Simulate Portfolio16.5 0001 11 -2 +0 0001 @@ -718,9 +718,9 @@

      Simulate Portfolio4746 3.535534 16.5 -0001 -12 -2 +0002 +1 +1 0001 @@ -728,9 +728,9 @@

      Simulate Portfolio4746 3.535534 16.5 -0001 -13 -3 +0002 +2 +1 0001 @@ -738,9 +738,9 @@

      Simulate Portfolio4746 3.535534 16.5 -0001 -14 +0002 3 +1 0001 @@ -748,9 +748,9 @@

      Simulate Portfolio4746 3.535534 16.5 -0001 -15 -3 +0002 +4 +2 0001 @@ -758,9 +758,9 @@

      Simulate Portfolio4746 3.535534 16.5 -0001 -16 -3 +0002 +5 +2 0001 @@ -768,8 +768,8 @@

      Simulate Portfolio4746 3.535534 16.5 -0001 -17 +0002 +6 3 @@ -778,9 +778,9 @@

      Simulate Portfolio4746 3.535534 16.5 -0001 -18 -3 +0002 +7 +4 0001 @@ -788,9 +788,9 @@

      Simulate Portfolio4746 3.535534 16.5 -0001 -19 -3 +0002 +8 +4 0001 @@ -799,8 +799,8 @@

      Simulate Portfolio3.535534 16.5 0002 -1 -1 +9 +5 0001 @@ -809,8 +809,8 @@

      Simulate Portfolio3.535534 16.5 0002 -2 -1 +10 +5 0001 @@ -819,8 +819,8 @@

      Simulate Portfolio3.535534 16.5 0002 -3 -2 +11 +6 0001 @@ -829,8 +829,8 @@

      Simulate Portfolio3.535534 16.5 0002 -4 -2 +12 +6 0001 @@ -839,8 +839,8 @@

      Simulate Portfolio3.535534 16.5 0002 -5 -2 +13 +6 0001 @@ -849,8 +849,8 @@

      Simulate Portfolio3.535534 16.5 0002 +14 6 -2 @@ -930,18 +930,18 @@

      ExampledfInput <- gsm::AE_Map_Raw(list(dfSUBJ = dfSUBJ_fix$data[[1]], dfAE = dfAE_fix$data[[1]])) dfInput

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    ## # A tibble: 113 × 5
    -##    SubjectID SiteID Exposure Count  Rate
    -##    <chr>     <chr>     <int> <int> <dbl>
    -##  1 0001      4746         19     3 0.158
    -##  2 0002      4746         24     8 0.333
    -##  3 0003      4747         26     6 0.231
    -##  4 0004      4747         28     7 0.25 
    -##  5 0005      4750         18     7 0.389
    -##  6 0006      4750         18     3 0.167
    -##  7 0007      4750         19     5 0.263
    -##  8 0008      4750         19     5 0.263
    -##  9 0009      4815          3     2 0.667
    -## 10 0010      4815         18     3 0.167
    +##    SubjectID SiteID Exposure Count   Rate
    +##    <chr>     <chr>     <int> <int>  <dbl>
    +##  1 0001      4746         11     0 0     
    +##  2 0002      4746         17     6 0.353 
    +##  3 0003      4747         27     6 0.222 
    +##  4 0004      4747         27     5 0.185 
    +##  5 0005      4750         18    10 0.556 
    +##  6 0006      4750         19     8 0.421 
    +##  7 0007      4750         19     8 0.421 
    +##  8 0008      4750         18     7 0.389 
    +##  9 0009      4815         15     3 0.2   
    +## 10 0010      4815         31     2 0.0645
     ## # ℹ 103 more rows
     dfTransformed <- gsm::Transform_Rate(
    @@ -953,16 +953,16 @@ 

    Example## # A tibble: 44 × 4 ## GroupID Numerator Denominator Metric ## <chr> <int> <int> <dbl> -## 1 4746 11 43 0.256 -## 2 4747 13 54 0.241 -## 3 4750 20 74 0.270 -## 4 4815 16 77 0.208 -## 5 4816 12 27 0.444 -## 6 4817 7 31 0.226 -## 7 4818 14 30 0.467 -## 8 4891 59 149 0.396 -## 9 4893 9 40 0.225 -## 10 4932 41 131 0.313 +## 1 4746 6 28 0.214 +## 2 4747 11 54 0.204 +## 3 4750 33 74 0.446 +## 4 4815 33 145 0.228 +## 5 4816 8 27 0.296 +## 6 4817 5 31 0.161 +## 7 4818 5 30 0.167 +## 8 4891 45 144 0.312 +## 9 4893 13 40 0.325 +## 10 4932 36 132 0.273 ## # ℹ 34 more rows

     dfAnalyzed <- gsm::Analyze_NormalApprox(dfTransformed)
    @@ -970,68 +970,68 @@ 

    Example## # A tibble: 44 × 7 ## GroupID Numerator Denominator Metric OverallMetric Factor Score ## <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> -## 1 5311 1 20 0.05 0.296 1.27 -2.14 -## 2 5084 2 21 0.0952 0.296 1.27 -1.79 -## 3 4815 16 77 0.208 0.296 1.27 -1.51 -## 4 5111 5 31 0.161 0.296 1.27 -1.46 -## 5 5229 13 61 0.213 0.296 1.27 -1.26 -## 6 5131 4 23 0.174 0.296 1.27 -1.14 -## 7 5308 14 61 0.230 0.296 1.27 -1.01 -## 8 4942 12 53 0.226 0.296 1.27 -0.987 -## 9 5166 24 98 0.245 0.296 1.27 -0.986 -## 10 4893 9 40 0.225 0.296 1.27 -0.875 +## 1 4815 33 145 0.228 0.300 1.31 -1.65 +## 2 4941 17 80 0.212 0.300 1.31 -1.48 +## 3 4817 5 31 0.161 0.300 1.31 -1.47 +## 4 4818 5 30 0.167 0.300 1.31 -1.39 +## 5 5079 20 89 0.225 0.300 1.31 -1.35 +## 6 4747 11 54 0.204 0.300 1.31 -1.34 +## 7 5311 3 20 0.15 0.300 1.31 -1.27 +## 8 5084 4 21 0.190 0.300 1.31 -0.953 +## 9 4943 5 25 0.2 0.300 1.31 -0.949 +## 10 4746 6 28 0.214 0.300 1.31 -0.860 ## # ℹ 34 more rows

     dfFlagged <- gsm::Flag_NormalApprox(dfAnalyzed, vThreshold = c(-3, -2, 2, 3))
     dfFlagged
    ## # A tibble: 44 × 8
    -##    GroupID Numerator Denominator Metric OverallMetric Factor  Score  Flag
    -##    <chr>       <int>       <int>  <dbl>         <dbl>  <dbl>  <dbl> <dbl>
    -##  1 4891           59         149 0.396          0.296   1.27  2.37      1
    -##  2 5311            1          20 0.05           0.296   1.27 -2.14     -1
    -##  3 5084            2          21 0.0952         0.296   1.27 -1.79      0
    -##  4 4815           16          77 0.208          0.296   1.27 -1.51      0
    -##  5 5111            5          31 0.161          0.296   1.27 -1.46      0
    -##  6 5229           13          61 0.213          0.296   1.27 -1.26      0
    -##  7 5131            4          23 0.174          0.296   1.27 -1.14      0
    -##  8 5308           14          61 0.230          0.296   1.27 -1.01      0
    -##  9 4942           12          53 0.226          0.296   1.27 -0.987     0
    -## 10 5166           24          98 0.245          0.296   1.27 -0.986     0
    +##    GroupID Numerator Denominator Metric OverallMetric Factor Score  Flag
    +##    <chr>       <int>       <int>  <dbl>         <dbl>  <dbl> <dbl> <dbl>
    +##  1 5168            9          15  0.6           0.300   1.31  2.22     1
    +##  2 5194           76         197  0.386         0.300   1.31  2.31     1
    +##  3 4750           33          74  0.446         0.300   1.31  2.40     1
    +##  4 4815           33         145  0.228         0.300   1.31 -1.65     0
    +##  5 4941           17          80  0.212         0.300   1.31 -1.48     0
    +##  6 4817            5          31  0.161         0.300   1.31 -1.47     0
    +##  7 4818            5          30  0.167         0.300   1.31 -1.39     0
    +##  8 5079           20          89  0.225         0.300   1.31 -1.35     0
    +##  9 4747           11          54  0.204         0.300   1.31 -1.34     0
    +## 10 5311            3          20  0.15          0.300   1.31 -1.27     0
     ## # ℹ 34 more rows
     dfSummary <- gsm::Summarize(dfFlagged)
     dfSummary
    ## # A tibble: 44 × 6
    -##    GroupID Numerator Denominator Metric  Score  Flag
    -##    <chr>       <int>       <int>  <dbl>  <dbl> <dbl>
    -##  1 4891           59         149  0.396  2.37      1
    -##  2 5311            1          20  0.05  -2.14     -1
    -##  3 4818           14          30  0.467  1.81      0
    -##  4 5135            8          18  0.444  1.22      0
    -##  5 4816           12          27  0.444  1.50      0
    -##  6 4969           25          62  0.403  1.64      0
    -##  7 5309           16          42  0.381  1.07      0
    -##  8 5265           18          48  0.375  1.06      0
    -##  9 5082           16          43  0.372  0.967     0
    -## 10 4967           25          68  0.368  1.15      0
    +##    GroupID Numerator Denominator Metric Score  Flag
    +##    <chr>       <int>       <int>  <dbl> <dbl> <dbl>
    +##  1 5168            9          15  0.6   2.22      1
    +##  2 4750           33          74  0.446 2.40      1
    +##  3 5194           76         197  0.386 2.31      1
    +##  4 5083           18          44  0.409 1.39      0
    +##  5 5265           16          42  0.381 1.01      0
    +##  6 5130           52         140  0.371 1.62      0
    +##  7 4986           21          58  0.362 0.908     0
    +##  8 5131            8          23  0.348 0.442     0
    +##  9 4985           38         111  0.342 0.860     0
    +## 10 5224            8          24  0.333 0.316     0
     ## # ℹ 34 more rows
     dfBounds <- gsm::Analyze_NormalApprox_PredictBounds(dfTransformed, vThreshold = c(-3, -2, 2, 3))
     dfBounds
    -
    ## # A tibble: 1,250 × 5
    +
    ## # A tibble: 1,246 × 5
     ##    Threshold Denominator LogDenominator Numerator  Metric
     ##        <dbl>       <dbl>          <dbl>     <dbl>   <dbl>
    -##  1        -3        27.4           3.31    0.0339 0.00124
    -##  2        -3        28.1           3.34    0.134  0.00479
    -##  3        -3        28.8           3.36    0.236  0.00821
    -##  4        -3        29.4           3.38    0.339  0.0115 
    -##  5        -3        30.1           3.40    0.443  0.0147 
    -##  6        -3        30.8           3.43    0.548  0.0178 
    -##  7        -3        31.4           3.45    0.654  0.0208 
    -##  8        -3        32.1           3.47    0.761  0.0237 
    -##  9        -3        32.8           3.49    0.869  0.0265 
    -## 10        -3        33.5           3.51    0.978  0.0292 
    -## # ℹ 1,240 more rows
    +## 1 -3 28.1 3.34 0.0765 0.00272 +## 2 -3 28.8 3.36 0.187 0.00649 +## 3 -3 29.6 3.39 0.299 0.0101 +## 4 -3 30.3 3.41 0.413 0.0136 +## 5 -3 31.0 3.43 0.527 0.0170 +## 6 -3 31.7 3.46 0.643 0.0203 +## 7 -3 32.5 3.48 0.760 0.0234 +## 8 -3 33.2 3.50 0.878 0.0264 +## 9 -3 33.9 3.52 0.997 0.0294 +## 10 -3 34.7 3.55 1.12 0.0322 +## # ℹ 1,236 more rows
     chart <- gsm::Visualize_Scatter(dfFlagged, dfBounds)
     chart
    @@ -1056,7 +1056,7 @@

    Simulate UR} sim_site_ur_gsm("4747", ur_rate = 0.75, dfTransformed)

    -
    ## [1] -2.975356
    +
    ## [1] -3.095796

    We write another function that systematically applies this sim_site_ur_gsm across all sites in all studies across a range of under-reporting ratios.

    @@ -1080,7 +1080,7 @@

    Simulate UR} df_sim_gsm_fix <- sim_ur_gsm(dfSUBJ_fix, dfAE_fix)

    -
    ## Warning: There were 3111 warnings in `mutate()`.
    +
    ## Warning: There were 3116 warnings in `mutate()`.
     ## The first warning was:
     ##  In argument: `z_i = ifelse(...)`.
     ## Caused by warning:
    @@ -1088,22 +1088,22 @@ 

    Simulate UR## In argument: `z_0 = ifelse(...)`. ## Caused by warning in `sqrt()`: ## ! NaNs produced -## Run `dplyr::last_dplyr_warnings()` to see the 3110 remaining warnings.

    +## Run `dplyr::last_dplyr_warnings()` to see the 3115 remaining warnings.
     df_sim_gsm_fix
    ## # A tibble: 129,966 × 5
     ##    study_id trans             siteid ur_rate  score
     ##    <chr>    <list>            <chr>    <dbl>  <dbl>
    -##  1 0001     <tibble [44 × 4]> 4746      0    -0.514
    -##  2 0001     <tibble [44 × 4]> 4746      0.1  -0.830
    -##  3 0001     <tibble [44 × 4]> 4746      0.25 -1.29 
    -##  4 0001     <tibble [44 × 4]> 4746      0.5  -2.02 
    -##  5 0001     <tibble [44 × 4]> 4746      0.75 -2.67 
    -##  6 0001     <tibble [44 × 4]> 4746      1    -3.24 
    -##  7 0001     <tibble [44 × 4]> 4747      0    -0.791
    -##  8 0001     <tibble [44 × 4]> 4747      0.1  -1.12 
    -##  9 0001     <tibble [44 × 4]> 4747      0.25 -1.59 
    -## 10 0001     <tibble [44 × 4]> 4747      0.5  -2.33 
    +##  1 0001     <tibble [44 × 4]> 4746      0    -0.860
    +##  2 0001     <tibble [44 × 4]> 4746      0.1  -1.07 
    +##  3 0001     <tibble [44 × 4]> 4746      0.25 -1.38 
    +##  4 0001     <tibble [44 × 4]> 4746      0.5  -1.87 
    +##  5 0001     <tibble [44 × 4]> 4746      0.75 -2.33 
    +##  6 0001     <tibble [44 × 4]> 4746      1    -2.75 
    +##  7 0001     <tibble [44 × 4]> 4747      0    -1.34 
    +##  8 0001     <tibble [44 × 4]> 4747      0.1  -1.61 
    +##  9 0001     <tibble [44 × 4]> 4747      0.25 -1.99 
    +## 10 0001     <tibble [44 × 4]> 4747      0.5  -2.57 
     ## # ℹ 129,956 more rows

    We repeat the same steps for the portfolio with the flexible AE rates.

    @@ -1113,7 +1113,7 @@

    Simulate URdf_sim_gsm_flex <- sim_ur_gsm(dfSUBJ_flex, dfAE_flex)

    -
    ## Warning: There were 3079 warnings in `mutate()`.
    +
    ## Warning: There were 3085 warnings in `mutate()`.
     ## The first warning was:
     ##  In argument: `z_i = ifelse(...)`.
     ## Caused by warning:
    @@ -1121,7 +1121,7 @@ 

    Simulate UR## In argument: `z_0 = ifelse(...)`. ## Caused by warning in `sqrt()`: ## ! NaNs produced -## Run `dplyr::last_dplyr_warnings()` to see the 3078 remaining warnings.

    +## Run `dplyr::last_dplyr_warnings()` to see the 3084 remaining warnings.
    @@ -1263,11 +1263,11 @@

    Table {gsm} - thresh: -2 AE rate: fix 0.00 -20848 -281 +20830 +299 532 21661 -0.013 +0.014 fpr 0.012 0.015 @@ -1276,73 +1276,73 @@

    Table {gsm} - thresh: -2 AE rate: fix 0.10 -19923 -1206 +19851 +1278 532 21661 -0.056 -tpr -0.053 0.059 +tpr +0.056 +0.062 {gsm} - thresh: -2 AE rate: fix 0.25 -15762 -5373 -526 +15848 +5282 +531 21661 -0.248 +0.244 tpr -0.242 -0.254 +0.238 +0.250 {gsm} - thresh: -2 AE rate: fix 0.50 -9127 -12016 -518 +9154 +11987 +520 21661 -0.555 +0.553 tpr -0.548 -0.561 +0.547 +0.560 {gsm} - thresh: -2 AE rate: fix 0.75 -5155 -15998 -508 +5214 +15941 +506 21661 -0.739 +0.736 tpr -0.733 -0.744 +0.730 +0.742 {gsm} - thresh: -2 AE rate: fix 1.00 -2938 -18228 +2915 +18251 495 21661 -0.842 +0.843 tpr -0.837 -0.846 +0.838 +0.847 {gsm} - thresh: -2 AE rate: flexible 0.00 -20901 -239 +20899 +241 521 21661 0.011 @@ -1354,8 +1354,8 @@

    Table {gsm} - thresh: -2 AE rate: flexible 0.10 -20665 -475 +20669 +471 521 21661 0.022 @@ -1367,9 +1367,9 @@

    Table {gsm} - thresh: -2 AE rate: flexible 0.25 -19740 -1400 -521 +19742 +1399 +520 21661 0.065 tpr @@ -1380,196 +1380,196 @@

    Table {gsm} - thresh: -2 AE rate: flexible 0.50 -16423 -4726 -512 +16448 +4698 +515 21661 -0.218 +0.217 tpr -0.213 -0.224 +0.211 +0.222 {gsm} - thresh: -2 AE rate: flexible 0.75 -12379 -8772 -510 +12320 +8830 +511 21661 -0.405 +0.408 tpr -0.398 -0.412 +0.401 +0.414 {gsm} - thresh: -2 AE rate: flexible 1.00 -8836 -12331 -494 +8727 +12437 +497 21661 -0.569 +0.574 tpr -0.563 -0.576 +0.568 +0.581 {simaerep} - thresh: 0.95 AE rate: fix 0.00 -21382 -279 +21367 +294 0 21661 -0.013 -fpr -0.011 0.014 +fpr +0.012 +0.015 {simaerep} - thresh: 0.95 AE rate: fix 0.10 -20442 -1219 +20366 +1295 0 21661 -0.056 +0.060 tpr -0.053 -0.059 +0.057 +0.063 {simaerep} - thresh: 0.95 AE rate: fix 0.25 -17079 -4582 +17155 +4506 0 21661 -0.212 +0.208 tpr -0.206 -0.217 +0.203 +0.214 {simaerep} - thresh: 0.95 AE rate: fix 0.50 -10623 -11038 +10635 +11026 0 21661 -0.510 +0.509 tpr -0.503 +0.502 0.516 {simaerep} - thresh: 0.95 AE rate: fix 0.75 -5895 -15766 +5845 +15816 0 21661 -0.728 +0.730 tpr -0.722 -0.734 +0.724 +0.736 {simaerep} - thresh: 0.95 AE rate: fix 1.00 -4066 -17595 +4053 +17608 0 21661 -0.812 +0.813 tpr -0.807 -0.817 +0.808 +0.818 {simaerep} - thresh: 0.95 AE rate: flexible 0.00 -21391 -270 +21340 +321 0 21661 -0.012 +0.015 fpr -0.011 -0.014 +0.013 +0.017 {simaerep} - thresh: 0.95 AE rate: flexible 0.10 -20321 -1340 +20234 +1427 0 21661 -0.062 +0.066 tpr -0.059 -0.065 +0.063 +0.069 {simaerep} - thresh: 0.95 AE rate: flexible 0.25 -16730 -4931 +16661 +5000 0 21661 -0.228 +0.231 tpr -0.222 -0.233 +0.225 +0.237 {simaerep} - thresh: 0.95 AE rate: flexible 0.50 -10052 -11609 +9878 +11783 0 21661 -0.536 +0.544 tpr -0.529 -0.543 +0.537 +0.551 {simaerep} - thresh: 0.95 AE rate: flexible 0.75 -5394 -16267 +5216 +16445 0 21661 -0.751 +0.759 tpr -0.745 -0.757 +0.753 +0.765 {simaerep} - thresh: 0.95 AE rate: flexible 1.00 -3738 -17923 +3595 +18066 0 21661 -0.827 +0.834 tpr -0.822 -0.832 +0.829 +0.839 @@ -1578,7 +1578,7 @@

    Table

    Plot Performance Metrics

    diff --git a/docs/articles/intro.html b/docs/articles/intro.html index f9e1a0a..6b4f36f 100644 --- a/docs/articles/intro.html +++ b/docs/articles/intro.html @@ -46,7 +46,7 @@ simaerep - 0.4.3 + 0.4.4 @@ -964,14 +964,6 @@

    Applying {simaerep}
     plot(aerep)
    ## study = NULL, defaulting to study:A
    -
    ## Warning: `all_equal()` was deprecated in dplyr 1.1.0.
    -##  Please use `all.equal()` instead.
    -##  And manually order the rows/cols as needed
    -##  The deprecated feature was likely used in the simaerep package.
    -##   Please report the issue to the authors.
    -## This warning is displayed once every 8 hours.
    -## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
    -## generated.

    For modifying the default parameters please check out the simaerep() documentation.

    diff --git a/docs/articles/portfolio_perf.html b/docs/articles/portfolio_perf.html index 0713eb5..b0dab2c 100644 --- a/docs/articles/portfolio_perf.html +++ b/docs/articles/portfolio_perf.html @@ -46,7 +46,7 @@ simaerep - 0.4.3 + 0.4.4 @@ -246,162 +246,162 @@

    Portfolio Configuration 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 10 0001 -0.4096706 +0.3746743 0002 -2.898275 -17.8 +4.452215 +18.4 10 0001 -0.4096706 +0.3746743 0003 -2.065591 -17.4 +4.802777 +18.8 10 0001 -0.4096706 +0.3746743 0004 -4.341019 -21.8 +4.571652 +18.3 10 0001 -0.4096706 +0.3746743 0005 -2.359378 +4.691600 20.3 10 0001 -0.4096706 +0.3746743 0006 -4.516636 -21.2 +2.884826 +19.1 10 0001 -0.4096706 +0.3746743 0007 -3.627059 -16.6 +3.829708 +18.0 10 0001 -0.4096706 +0.3746743 0008 -2.973961 -17.8 +5.185450 +21.0 10 0001 -0.4096706 +0.3746743 0009 -2.319004 -16.4 +4.948625 +19.4 10 0001 -0.4096706 +0.3746743 0010 -5.028806 -19.8 +4.115013 +19.4 10 0002 -0.4755621 +0.5005081 0001 -6.929005 -19.3 +4.552167 +19.5 10 0002 -0.4755621 +0.5005081 0002 -3.233505 -19.3 +4.321779 +18.7 10 0002 -0.4755621 +0.5005081 0003 -4.623611 -20.4 +3.333333 +18.0 10 0002 -0.4755621 +0.5005081 0004 -3.881580 -21.2 +3.772709 +21.3 10 0002 -0.4755621 +0.5005081 0005 -6.019413 -19.3 +3.695342 +21.1 10 0002 -0.4755621 +0.5005081 0006 -3.921451 -21.6 +3.951090 +17.5 10 0002 -0.4755621 +0.5005081 0007 -6.963396 -20.6 +4.104198 +21.2 10 0002 -0.4755621 +0.5005081 0008 -4.894441 -21.8 +4.391912 +19.2 10 0002 -0.4755621 +0.5005081 0009 -3.333333 -21.0 +3.374743 +19.5 10 0002 -0.4755621 +0.5005081 0010 -2.469818 -20.1 +3.521363 +20.8 10 @@ -442,253 +442,253 @@

    Simulate Portfolio from Configura 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0001 1 -1 +0 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0001 2 1 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0001 3 1 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0001 4 1 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0001 5 1 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0001 6 -2 +1 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0001 7 -2 +1 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0001 8 -2 +1 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0001 9 2 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0001 10 -3 +2 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0001 11 -3 +2 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0001 12 -4 +3 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0001 13 4 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0001 14 4 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0001 15 -4 +5 0001 -0.4096706 -0001 -3.573047 -19.1 +0.3746743 0001 -16 -5 +3.794733 +19.2 +0002 +1 +1 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 -0001 -17 -7 +3.794733 +19.2 +0002 +2 +1 0001 -0.4096706 -0001 -3.573047 -19.1 +0.3746743 0001 -18 -7 +3.794733 +19.2 +0002 +3 +1 0001 -0.4096706 -0001 -3.573047 -19.1 +0.3746743 0001 -19 -8 +3.794733 +19.2 +0002 +4 +2 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0002 -1 -0 +5 +3 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0002 -2 -1 +6 +4 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0002 -3 -1 +7 +4 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0002 +8 4 -2 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0002 -5 -2 +9 +4 0001 -0.4096706 +0.3746743 0001 -3.573047 -19.1 +3.794733 +19.2 0002 -6 -4 +10 +5 @@ -1070,7 +1070,7 @@

    Simulate Portfolio622 0001 2 -0 +1 0001 @@ -1135,7 +1135,7 @@

    Simulate Portfolio622 0001 7 -2 +1 0001 @@ -1148,7 +1148,7 @@

    Simulate Portfolio622 0001 8 -3 +1 0001 @@ -1161,7 +1161,7 @@

    Simulate Portfolio622 0001 9 -3 +1 0001 @@ -1174,7 +1174,7 @@

    Simulate Portfolio622 0001 10 -3 +1 0001 @@ -1187,7 +1187,7 @@

    Simulate Portfolio622 0001 11 -3 +1 0001 @@ -1200,7 +1200,7 @@

    Simulate Portfolio622 0001 12 -3 +1 0001 @@ -1213,7 +1213,7 @@

    Simulate Portfolio622 0001 13 -3 +1 0001 @@ -1226,7 +1226,7 @@

    Simulate Portfolio622 0001 14 -3 +1 0001 @@ -1239,7 +1239,7 @@

    Simulate Portfolio622 0001 15 -4 +1 0001 @@ -1252,7 +1252,7 @@

    Simulate Portfolio622 0001 16 -5 +1 0001 @@ -1265,7 +1265,7 @@

    Simulate Portfolio622 0001 17 -5 +1 0001 @@ -1278,7 +1278,7 @@

    Simulate Portfolio622 0001 18 -5 +1 0001 @@ -1291,7 +1291,7 @@

    Simulate Portfolio622 0001 19 -5 +1 0001 @@ -1304,7 +1304,7 @@

    Simulate Portfolio622 0001 20 -5 +2 0001 @@ -1317,7 +1317,7 @@

    Simulate Portfolio622 0001 21 -6 +3 0001 @@ -1343,7 +1343,7 @@

    Simulate Portfolio622 0002 2 -1 +0 0001 @@ -1356,7 +1356,7 @@

    Simulate Portfolio622 0002 3 -1 +0 0001 @@ -1369,7 +1369,7 @@

    Simulate Portfolio622 0002 4 -1 +0 @@ -4410,16 +4410,16 @@

    Heuristic Rank## # A tibble: 15,674 × 6 ## study_id site_number visit n_ae ae_per_visit ls_study_ae_per_visit ## <chr> <chr> <int> <int> <dbl> <list> -## 1 0001 0001 42 11 0.262 <dbl [43]> -## 2 0001 0002 55 15 0.273 <dbl [43]> -## 3 0001 0003 74 24 0.324 <dbl [43]> -## 4 0001 0004 170 37 0.218 <dbl [43]> -## 5 0001 0005 30 10 0.333 <dbl [43]> -## 6 0001 0006 35 6 0.171 <dbl [43]> -## 7 0001 0007 36 14 0.389 <dbl [43]> -## 8 0001 0008 206 62 0.301 <dbl [43]> -## 9 0001 0009 40 18 0.45 <dbl [43]> -## 10 0001 0010 141 34 0.241 <dbl [43]> +## 1 0001 0001 42 10 0.238 <dbl [43]> +## 2 0001 0002 60 14 0.233 <dbl [43]> +## 3 0001 0003 78 25 0.321 <dbl [43]> +## 4 0001 0004 189 43 0.228 <dbl [43]> +## 5 0001 0005 30 7 0.233 <dbl [43]> +## 6 0001 0006 35 9 0.257 <dbl [43]> +## 7 0001 0007 36 9 0.25 <dbl [43]> +## 8 0001 0008 192 64 0.333 <dbl [43]> +## 9 0001 0009 40 8 0.2 <dbl [43]> +## 10 0001 0010 134 30 0.224 <dbl [43]> ## # ℹ 15,664 more rows

    We write a function that: - determines how many sites should be flagged in a study - pools ae_per_visit rates and ranks sites (using @@ -4475,11 +4475,11 @@

    Heuristic Rank## # A tibble: 6 × 3 ## ur_rate ae_per_visit is_ur ## <dbl> <dbl> <lgl> -## 1 0 0.262 FALSE -## 2 0.1 0.236 FALSE -## 3 0.25 0.196 FALSE -## 4 0.5 0.131 TRUE -## 5 0.75 0.0655 TRUE +## 1 0 0.238 FALSE +## 2 0.1 0.214 FALSE +## 3 0.25 0.179 FALSE +## 4 0.5 0.119 TRUE +## 5 0.75 0.0595 TRUE ## 6 1 0 TRUE

    We apply.

    diff --git a/docs/articles/portfolio_perf_files/figure-html/check_portf-1.png b/docs/articles/portfolio_perf_files/figure-html/check_portf-1.png
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    diff --git a/docs/articles/usability_limits.html b/docs/articles/usability_limits.html index 24be446..45122c0 100644 --- a/docs/articles/usability_limits.html +++ b/docs/articles/usability_limits.html @@ -46,7 +46,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/articles/visit_med75.html b/docs/articles/visit_med75.html index bd96736..9779a79 100644 --- a/docs/articles/visit_med75.html +++ b/docs/articles/visit_med75.html @@ -46,7 +46,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/articles/visits_or_days.html b/docs/articles/visits_or_days.html index 2548575..0a3940c 100644 --- a/docs/articles/visits_or_days.html +++ b/docs/articles/visits_or_days.html @@ -46,7 +46,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/authors.html b/docs/authors.html index dd05dfb..3a3a274 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 @@ -113,13 +113,13 @@

    Citation

    Koneswarakantha B (2024). simaerep: Find Clinical Trial Sites Under-Reporting Adverse Events. -R package version 0.4.3, https://github.com/openpharma/simaerep, https://openpharma.github.io/simaerep/. +R package version 0.4.4, https://github.com/openpharma/simaerep, https://openpharma.github.io/simaerep/.

    @Manual{,
       title = {simaerep: Find Clinical Trial Sites Under-Reporting Adverse Events},
       author = {Bjoern Koneswarakantha},
       year = {2024},
    -  note = {R package version 0.4.3, https://github.com/openpharma/simaerep},
    +  note = {R package version 0.4.4, https://github.com/openpharma/simaerep},
       url = {https://openpharma.github.io/simaerep/},
     }
    diff --git a/docs/index.html b/docs/index.html index 8ac4dc6..373fddb 100644 --- a/docs/index.html +++ b/docs/index.html @@ -50,7 +50,7 @@ simaerep - 0.4.3 + 0.4.4 @@ -395,12 +395,7 @@

    Application aerep <- simaerep(df_visit) -plot(aerep, study = "A") -#> Warning: `all_equal()` was deprecated in dplyr 1.1.0. -#> ℹ Please use `all.equal()` instead. -#> ℹ And manually order the rows/cols as needed -#> ℹ The deprecated feature was likely used in the simaerep package. -#> Please report the issue to the authors. +plot(aerep, study = "A")

    Left panel shows mean AE reporting per site (lightblue and darkblue lines) against mean AE reporting of the entire study (golden line). Single sites are plotted in descending order by AE under-reporting probability on the right panel in which grey lines denote cumulative AE count of single patients. Grey dots in the left panel plot indicate sites that were picked for single plotting. AE under-reporting probability of dark blue lines crossed threshold of 95%. Numbers in the upper left corner indicate the ratio of patients that have been used for the analysis against the total number of patients. Patients that have not been on the study long enough to reach the evaluation point (visit_med75, see introduction) will be ignored.

    diff --git a/docs/news/index.html b/docs/news/index.html index c564c0b..d19c200 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 @@ -93,6 +93,12 @@

    Changelog

    Source: NEWS.md +
    + +
    • allow flexible AE rates in data simulations
    • +
    • add vignette comparing simaerep to gsm performance
    • +
    • fix dplyr warnings
    • +
    • delete performance unit tests (poisson faster than bootstrap) to accommodate CRAN request
    • diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 3b6a57d..746a6e3 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -10,7 +10,7 @@ articles: usability_limits: usability_limits.html visit_med75: visit_med75.html visits_or_days: visits_or_days.html -last_built: 2024-02-07T09:37Z +last_built: 2024-02-22T10:42Z urls: reference: https://openpharma.github.io/simaerep/reference article: https://openpharma.github.io/simaerep/articles diff --git a/docs/reference/aggr_duplicated_visits.html b/docs/reference/aggr_duplicated_visits.html index 5ac3d04..572dbec 100644 --- a/docs/reference/aggr_duplicated_visits.html +++ b/docs/reference/aggr_duplicated_visits.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4
    diff --git a/docs/reference/check_df_visit.html b/docs/reference/check_df_visit.html index 710756d..82c9bed 100644 --- a/docs/reference/check_df_visit.html +++ b/docs/reference/check_df_visit.html @@ -25,7 +25,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/eval_sites.html b/docs/reference/eval_sites.html index 564f89b..5feb20f 100644 --- a/docs/reference/eval_sites.html +++ b/docs/reference/eval_sites.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/eval_sites_deprecated.html b/docs/reference/eval_sites_deprecated.html index 5f46697..f4e8b5e 100644 --- a/docs/reference/eval_sites_deprecated.html +++ b/docs/reference/eval_sites_deprecated.html @@ -25,7 +25,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/exp_implicit_missing_visits.html b/docs/reference/exp_implicit_missing_visits.html index b2eaef7..89364c5 100644 --- a/docs/reference/exp_implicit_missing_visits.html +++ b/docs/reference/exp_implicit_missing_visits.html @@ -23,7 +23,7 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zXFT8eEJQy=zOfWNiZalomc;NWNZ3|c{E+{}Z{>JnT;KGt?K5WZ%3~_fTH~bRr z_F%b3&iwuJ=meu2?HK;@fhSVPL7^>u%CR7>)Hs$o=Tv>W)4}5yN_D58qRjnyxnGp%(&WVQv61KT&Em zKfdzr>{aCLij}pW^ZjKqmKEPz%0}!!MC<&$w^i(S$1c<3mPhcuUVUXXkh~VW=}>p80!C@=G$zy4`>o516wlaz(e-}LaXrHjo|+PbN!cK diff --git a/docs/reference/get_config.html b/docs/reference/get_config.html index d721ffd..ca36e64 100644 --- a/docs/reference/get_config.html +++ b/docs/reference/get_config.html @@ -27,7 +27,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/get_ecd_values.html b/docs/reference/get_ecd_values.html index 6d11bff..37f1a98 100644 --- a/docs/reference/get_ecd_values.html +++ b/docs/reference/get_ecd_values.html @@ -27,7 +27,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/get_pat_pool_config.html b/docs/reference/get_pat_pool_config.html index 235dbb9..ea36347 100644 --- a/docs/reference/get_pat_pool_config.html +++ b/docs/reference/get_pat_pool_config.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/get_portf_perf.html b/docs/reference/get_portf_perf.html index 96ad6b4..487734c 100644 --- a/docs/reference/get_portf_perf.html +++ b/docs/reference/get_portf_perf.html @@ -24,7 +24,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/get_site_mean_ae_dev.html b/docs/reference/get_site_mean_ae_dev.html index 851340d..8fd1127 100644 --- a/docs/reference/get_site_mean_ae_dev.html +++ b/docs/reference/get_site_mean_ae_dev.html @@ -24,7 +24,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/get_visit_med75.html b/docs/reference/get_visit_med75.html index 1d3968e..07dd3de 100644 --- a/docs/reference/get_visit_med75.html +++ b/docs/reference/get_visit_med75.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/index.html b/docs/reference/index.html index b13a79a..56a2716 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/is_orivisit.html b/docs/reference/is_orivisit.html index 0aa258d..e84811d 100644 --- a/docs/reference/is_orivisit.html +++ b/docs/reference/is_orivisit.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/is_simaerep.html b/docs/reference/is_simaerep.html index b8032de..21030e7 100644 --- a/docs/reference/is_simaerep.html +++ b/docs/reference/is_simaerep.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/orivisit.html b/docs/reference/orivisit.html index c0f208c..08d6e14 100644 --- a/docs/reference/orivisit.html +++ b/docs/reference/orivisit.html @@ -24,7 +24,7 @@ simaerep - 0.4.3 + 0.4.4 @@ -151,11 +151,6 @@

      Examples

      #> 2288 bytes as.data.frame(visit) -#> Warning: `all_equal()` was deprecated in dplyr 1.1.0. -#> Please use `all.equal()` instead. -#> And manually order the rows/cols as needed -#> The deprecated feature was likely used in the simaerep package. -#> Please report the issue to the authors. #> # A tibble: 1,970 × 9 #> patnum site_number is_ur max_visit_mean max_visit_sd ae_per_visit_mean visit #> <chr> <chr> <lgl> <dbl> <dbl> <dbl> <int> diff --git a/docs/reference/pat_aggr.html b/docs/reference/pat_aggr.html index 1dfbec3..77dff76 100644 --- a/docs/reference/pat_aggr.html +++ b/docs/reference/pat_aggr.html @@ -24,7 +24,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/pat_pool.html b/docs/reference/pat_pool.html index 6d2aed0..3d76bd2 100644 --- a/docs/reference/pat_pool.html +++ b/docs/reference/pat_pool.html @@ -26,7 +26,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/pipe.html b/docs/reference/pipe.html index 408d0c1..f659821 100644 --- a/docs/reference/pipe.html +++ b/docs/reference/pipe.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/plot.simaerep.html b/docs/reference/plot.simaerep.html index 86fbb05..01e9071 100644 --- a/docs/reference/plot.simaerep.html +++ b/docs/reference/plot.simaerep.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/plot_dots.html b/docs/reference/plot_dots.html index 089bfc1..be488bd 100644 --- a/docs/reference/plot_dots.html +++ b/docs/reference/plot_dots.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/plot_sim_example.html b/docs/reference/plot_sim_example.html index 2f541bd..3355495 100644 --- a/docs/reference/plot_sim_example.html +++ b/docs/reference/plot_sim_example.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/plot_sim_examples.html b/docs/reference/plot_sim_examples.html index 611a6de..d488552 100644 --- a/docs/reference/plot_sim_examples.html +++ b/docs/reference/plot_sim_examples.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/plot_study.html b/docs/reference/plot_study.html index 11ca9b1..efce2d1 100644 --- a/docs/reference/plot_study.html +++ b/docs/reference/plot_study.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/plot_visit_med75.html b/docs/reference/plot_visit_med75.html index 0bff9c3..8d64433 100644 --- a/docs/reference/plot_visit_med75.html +++ b/docs/reference/plot_visit_med75.html @@ -24,7 +24,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/poiss_test_site_ae_vs_study_ae.html b/docs/reference/poiss_test_site_ae_vs_study_ae.html index 6bddb30..273b862 100644 --- a/docs/reference/poiss_test_site_ae_vs_study_ae.html +++ b/docs/reference/poiss_test_site_ae_vs_study_ae.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/prep_for_sim.html b/docs/reference/prep_for_sim.html index 53ebcd9..9ba0ada 100644 --- a/docs/reference/prep_for_sim.html +++ b/docs/reference/prep_for_sim.html @@ -25,7 +25,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/prob_lower_site_ae_vs_study_ae.html b/docs/reference/prob_lower_site_ae_vs_study_ae.html index d43a211..c605d2b 100644 --- a/docs/reference/prob_lower_site_ae_vs_study_ae.html +++ b/docs/reference/prob_lower_site_ae_vs_study_ae.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/purrr_bar.html b/docs/reference/purrr_bar.html index a775feb..d827235 100644 --- a/docs/reference/purrr_bar.html +++ b/docs/reference/purrr_bar.html @@ -25,7 +25,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/sim_after_prep.html b/docs/reference/sim_after_prep.html index 36cc026..796f668 100644 --- a/docs/reference/sim_after_prep.html +++ b/docs/reference/sim_after_prep.html @@ -24,7 +24,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/sim_scenario.html b/docs/reference/sim_scenario.html index 3c9c940..267e268 100644 --- a/docs/reference/sim_scenario.html +++ b/docs/reference/sim_scenario.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/sim_sites.html b/docs/reference/sim_sites.html index 3e49bf8..bac4a86 100644 --- a/docs/reference/sim_sites.html +++ b/docs/reference/sim_sites.html @@ -25,7 +25,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/sim_studies.html b/docs/reference/sim_studies.html index 6da3ce6..853bade 100644 --- a/docs/reference/sim_studies.html +++ b/docs/reference/sim_studies.html @@ -25,7 +25,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/sim_test_data_patient.html b/docs/reference/sim_test_data_patient.html index ce8d012..c2da37b 100644 --- a/docs/reference/sim_test_data_patient.html +++ b/docs/reference/sim_test_data_patient.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/sim_test_data_portfolio.html b/docs/reference/sim_test_data_portfolio.html index f470e8a..7b94b1f 100644 --- a/docs/reference/sim_test_data_portfolio.html +++ b/docs/reference/sim_test_data_portfolio.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 @@ -99,7 +99,12 @@

      Simulate Portfolio Test Data

      -
      sim_test_data_portfolio(df_config, parallel = FALSE, progress = TRUE)
      +
      sim_test_data_portfolio(
      +  df_config,
      +  df_ae_rates = NULL,
      +  parallel = FALSE,
      +  progress = TRUE
      +)
      @@ -108,6 +113,10 @@

      Arguments

      dataframe as returned by get_config

      +
      df_ae_rates
      +

      dataframe with ae rates. Default: NULL

      + +
      parallel

      logical activate parallel processing, see details, Default: FALSE

      diff --git a/docs/reference/sim_test_data_study.html b/docs/reference/sim_test_data_study.html index 1bd2777..e1d9160 100644 --- a/docs/reference/sim_test_data_study.html +++ b/docs/reference/sim_test_data_study.html @@ -25,7 +25,7 @@ simaerep - 0.4.3 + 0.4.4
      @@ -110,7 +110,8 @@

      simulate study test data

      ur_rate = 0, max_visit_mean = 20, max_visit_sd = 4, - ae_per_visit_mean = 0.5 + ae_per_visit_mean = 0.5, + ae_rates = NULL ) @@ -146,6 +147,10 @@

      Arguments

      ae_per_visit_mean

      mean ae per visit per patient, Default: 0.5

      + +
      ae_rates
      +

      vector with visit-specific ae rates, Default: Null

      +

      Value

      @@ -206,6 +211,23 @@

      Examples

      #> 10 P000001 S0001 TRUE 20 4 0.25 10 #> # ℹ 13 more rows #> # ℹ 1 more variable: n_ae <int> +ae_rates <- c(0.7, rep(0.5, 8), rep(0.3, 5)) +sim_test_data_study(n_pat = 100, n_sites = 5, ae_rates = ae_rates) +#> # A tibble: 1,968 × 8 +#> patnum site_number is_ur max_visit_mean max_visit_sd ae_per_visit_mean visit +#> <chr> <chr> <lgl> <dbl> <dbl> <dbl> <int> +#> 1 P000001 S0001 FALSE 20 4 0.443 1 +#> 2 P000001 S0001 FALSE 20 4 0.443 2 +#> 3 P000001 S0001 FALSE 20 4 0.443 3 +#> 4 P000001 S0001 FALSE 20 4 0.443 4 +#> 5 P000001 S0001 FALSE 20 4 0.443 5 +#> 6 P000001 S0001 FALSE 20 4 0.443 6 +#> 7 P000001 S0001 FALSE 20 4 0.443 7 +#> 8 P000001 S0001 FALSE 20 4 0.443 8 +#> 9 P000001 S0001 FALSE 20 4 0.443 9 +#> 10 P000001 S0001 FALSE 20 4 0.443 10 +#> # ℹ 1,958 more rows +#> # ℹ 1 more variable: n_ae <int>
      diff --git a/docs/reference/sim_ur_scenarios.html b/docs/reference/sim_ur_scenarios.html index 5269b91..ca0189c 100644 --- a/docs/reference/sim_ur_scenarios.html +++ b/docs/reference/sim_ur_scenarios.html @@ -24,7 +24,7 @@ simaerep - 0.4.3 + 0.4.4 @@ -275,44 +275,44 @@

      Examples

      #> # A tibble: 20 × 6 #> study_id ae_per_visit_mean site_number max_visit_sd max_visit_mean n_pat #> <chr> <dbl> <chr> <dbl> <dbl> <int> -#> 1 0001 0.416 0001 3.39 18.2 10 -#> 2 0001 0.416 0002 2.72 19.6 10 -#> 3 0001 0.416 0003 2.12 19.6 10 -#> 4 0001 0.416 0004 3.92 18.4 10 -#> 5 0001 0.416 0005 3.63 19.9 10 -#> 6 0001 0.416 0006 3.83 20 10 -#> 7 0001 0.416 0007 2.69 21.1 10 -#> 8 0001 0.416 0008 4.24 19 10 -#> 9 0001 0.416 0009 3.59 21.3 10 -#> 10 0001 0.416 0010 2.95 19.7 10 -#> 11 0002 0.468 0001 4.42 20 10 -#> 12 0002 0.468 0002 4.03 21.4 10 -#> 13 0002 0.468 0003 4.13 20.2 10 -#> 14 0002 0.468 0004 2.58 18.3 10 -#> 15 0002 0.468 0005 4.64 17.8 10 -#> 16 0002 0.468 0006 2.37 17.6 10 -#> 17 0002 0.468 0007 4.80 19.8 10 -#> 18 0002 0.468 0008 2 20 10 -#> 19 0002 0.468 0009 3.17 19.5 10 -#> 20 0002 0.468 0010 6.57 19.9 10 +#> 1 0001 0.366 0001 4.42 20 10 +#> 2 0001 0.366 0002 4.03 21.4 10 +#> 3 0001 0.366 0003 4.13 20.2 10 +#> 4 0001 0.366 0004 2.58 18.3 10 +#> 5 0001 0.366 0005 4.64 17.8 10 +#> 6 0001 0.366 0006 2.37 17.6 10 +#> 7 0001 0.366 0007 4.80 19.8 10 +#> 8 0001 0.366 0008 2 20 10 +#> 9 0001 0.366 0009 3.17 19.5 10 +#> 10 0001 0.366 0010 6.57 19.9 10 +#> 11 0002 0.489 0001 2.85 19.9 10 +#> 12 0002 0.489 0002 3.31 18.1 10 +#> 13 0002 0.489 0003 3.14 18.1 10 +#> 14 0002 0.489 0004 4.74 20.7 10 +#> 15 0002 0.489 0005 5.20 19.2 10 +#> 16 0002 0.489 0006 3.30 21 10 +#> 17 0002 0.489 0007 4.07 19.9 10 +#> 18 0002 0.489 0008 4.53 18.5 10 +#> 19 0002 0.489 0009 2.95 21.7 10 +#> 20 0002 0.489 0010 3.36 20.2 10 df_portf <- sim_test_data_portfolio(df_config) df_portf -#> # A tibble: 3,815 × 8 +#> # A tibble: 3,834 × 8 #> study_id ae_per_visit_mean site_number max_visit_sd max_visit_mean patnum #> <chr> <dbl> <chr> <dbl> <dbl> <chr> -#> 1 0001 0.416 0001 3.39 18.2 0001 -#> 2 0001 0.416 0001 3.39 18.2 0001 -#> 3 0001 0.416 0001 3.39 18.2 0001 -#> 4 0001 0.416 0001 3.39 18.2 0001 -#> 5 0001 0.416 0001 3.39 18.2 0001 -#> 6 0001 0.416 0001 3.39 18.2 0001 -#> 7 0001 0.416 0001 3.39 18.2 0001 -#> 8 0001 0.416 0001 3.39 18.2 0001 -#> 9 0001 0.416 0001 3.39 18.2 0001 -#> 10 0001 0.416 0001 3.39 18.2 0001 -#> # ℹ 3,805 more rows +#> 1 0001 0.366 0001 4.42 20 0001 +#> 2 0001 0.366 0001 4.42 20 0001 +#> 3 0001 0.366 0001 4.42 20 0001 +#> 4 0001 0.366 0001 4.42 20 0001 +#> 5 0001 0.366 0001 4.42 20 0001 +#> 6 0001 0.366 0001 4.42 20 0001 +#> 7 0001 0.366 0001 4.42 20 0001 +#> 8 0001 0.366 0001 4.42 20 0001 +#> 9 0001 0.366 0001 4.42 20 0001 +#> 10 0001 0.366 0001 4.42 20 0001 +#> # ℹ 3,824 more rows #> # ℹ 2 more variables: visit <int>, n_ae <int> df_scen <- sim_ur_scenarios(df_portf, @@ -329,16 +329,16 @@

      Examples

      #> # A tibble: 140 × 14 #> study_id site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75 #> <chr> <chr> <int> <int> <dbl> <dbl> -#> 1 0001 0001 10 10 12 5.9 -#> 2 0001 0001 10 10 12 2.95 -#> 3 0001 0001 10 10 12 0 -#> 4 0001 0001 10 10 12 2.95 -#> 5 0001 0001 10 10 12 0 -#> 6 0001 0001 10 10 12 2.95 -#> 7 0001 0001 10 10 12 0 -#> 8 0001 0002 10 9 15 4.78 -#> 9 0001 0002 10 9 15 2.39 -#> 10 0001 0002 10 9 15 0 +#> 1 0001 0001 10 9 16 5.44 +#> 2 0001 0001 10 9 16 2.72 +#> 3 0001 0001 10 9 16 0 +#> 4 0001 0001 10 9 16 2.72 +#> 5 0001 0001 10 9 16 0 +#> 6 0001 0001 10 9 16 2.72 +#> 7 0001 0001 10 9 16 0 +#> 8 0001 0002 10 9 16 4.89 +#> 9 0001 0002 10 9 16 2.44 +#> 10 0001 0002 10 9 16 0 #> # ℹ 130 more rows #> # ℹ 8 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>, #> # extra_ur_sites <dbl>, frac_pat_with_ur <dbl>, ur_rate <dbl>, @@ -350,16 +350,16 @@

      Examples

      #> # A tibble: 27 × 5 #> fpr thresh extra_ur_sites ur_rate tpr #> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 0.001 0.990 0 0 0.05 -#> 2 0.001 0.990 1 0 0.05 -#> 3 0.001 0.990 2 0 0.05 -#> 4 0.001 0.990 0 0.5 1 -#> 5 0.001 0.990 1 0.5 0.9 -#> 6 0.001 0.990 2 0.5 0.85 -#> 7 0.001 0.990 0 1 1 -#> 8 0.001 0.990 1 1 1 -#> 9 0.001 0.990 2 1 1 -#> 10 0.01 0.961 0 0 0.05 +#> 1 0.001 0.858 0 0 0.05 +#> 2 0.001 0.858 1 0 0.05 +#> 3 0.001 0.858 2 0 0.05 +#> 4 0.001 0.858 0 0.5 1 +#> 5 0.001 0.858 1 0.5 1 +#> 6 0.001 0.858 2 0.5 1 +#> 7 0.001 0.858 0 1 1 +#> 8 0.001 0.858 1 1 1 +#> 9 0.001 0.858 2 1 1 +#> 10 0.01 0.857 0 0 0.05 #> # ℹ 17 more rows # } diff --git a/docs/reference/simaerep.html b/docs/reference/simaerep.html index 5a2c252..651aeac 100644 --- a/docs/reference/simaerep.html +++ b/docs/reference/simaerep.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/site_aggr.html b/docs/reference/site_aggr.html index 57e3502..e6da9ee 100644 --- a/docs/reference/site_aggr.html +++ b/docs/reference/site_aggr.html @@ -23,7 +23,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/docs/reference/with_progress_cnd.html b/docs/reference/with_progress_cnd.html index 39e9991..765d610 100644 --- a/docs/reference/with_progress_cnd.html +++ b/docs/reference/with_progress_cnd.html @@ -25,7 +25,7 @@ simaerep - 0.4.3 + 0.4.4 diff --git a/inst/WORDLIST b/inst/WORDLIST index aba68af..bd99f85 100644 --- a/inst/WORDLIST +++ b/inst/WORDLIST @@ -12,9 +12,11 @@ Hoffmann Lifecycle Modelling Ménard +RBQM Saf TP Uncompliant +Zink ae aes aggr @@ -28,6 +30,7 @@ csv darkblue df doi +dplyr ecd ecdf et @@ -38,7 +41,7 @@ frac furrr ggplot grey -https +gsm infer’ iterable iterables diff --git a/man/figures/README-unnamed-chunk-2-1.png b/man/figures/README-unnamed-chunk-2-1.png index ca2b821b3d06045687ee022f964b8e2e8c241eab..c95aec5cec923cf184c40aacf3674d2230cbaf3b 100644 GIT binary patch literal 191425 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zXFT8eEJQy=zOfWNiZalomc;NWNZ3|c{E+{}Z{>JnT;KGt?K5WZ%3~_fTH~bRr z_F%b3&iwuJ=meu2?HK;@fhSVPL7^>u%CR7>)Hs$o=Tv>W)4}5yN_D58qRjnyxnGp%(&WVQv61KT&Em zKfdzr>{aCLij}pW^ZjKqmKEPz%0}!!MC<&$w^i(S$1c<3mPhcuUVUXXkh~VW=}>p80!C@=G$zy4`>o516wlaz(e-}LaXrHjo|+PbN!cK diff --git a/man/sim_test_data_portfolio.Rd b/man/sim_test_data_portfolio.Rd index 753e7b1..b22b850 100644 --- a/man/sim_test_data_portfolio.Rd +++ b/man/sim_test_data_portfolio.Rd @@ -4,11 +4,18 @@ \alias{sim_test_data_portfolio} \title{Simulate Portfolio Test Data} \usage{ -sim_test_data_portfolio(df_config, parallel = FALSE, progress = TRUE) +sim_test_data_portfolio( + df_config, + df_ae_rates = NULL, + parallel = FALSE, + progress = TRUE +) } \arguments{ \item{df_config}{dataframe as returned by \code{\link{get_config}}} +\item{df_ae_rates}{dataframe with ae rates. Default: NULL} + \item{parallel}{logical activate parallel processing, see details, Default: FALSE} \item{progress}{logical, Default: TRUE} diff --git a/man/sim_test_data_study.Rd b/man/sim_test_data_study.Rd index 406b615..12822f8 100644 --- a/man/sim_test_data_study.Rd +++ b/man/sim_test_data_study.Rd @@ -11,7 +11,8 @@ sim_test_data_study( ur_rate = 0, max_visit_mean = 20, max_visit_sd = 4, - ae_per_visit_mean = 0.5 + ae_per_visit_mean = 0.5, + ae_rates = NULL ) } \arguments{ @@ -31,6 +32,8 @@ Default: 20} patient, Default: 4} \item{ae_per_visit_mean}{mean ae per visit per patient, Default: 0.5} + +\item{ae_rates}{vector with visit-specific ae rates, Default: Null} } \value{ tibble with columns site_number, patnum, is_ur, max_visit_mean, @@ -54,4 +57,6 @@ df_visit[which(df_visit$patnum == "P000001"),] df_visit <- sim_test_data_study(n_pat = 100, n_sites = 5, frac_site_with_ur = 0.2, ur_rate = 0.5) df_visit[which(df_visit$patnum == "P000001"),] +ae_rates <- c(0.7, rep(0.5, 8), rep(0.3, 5)) +sim_test_data_study(n_pat = 100, n_sites = 5, ae_rates = ae_rates) } diff --git a/tests/testthat/_snaps/eval_sites.md b/tests/testthat/_snaps/eval_sites.md index 4dadce6..a446ccd 100644 --- a/tests/testthat/_snaps/eval_sites.md +++ b/tests/testthat/_snaps/eval_sites.md @@ -2,11 +2,13 @@ Code df_eval <- eval_sites(df_sim_sites, r_sim_sites = 100) - Warning + Condition + Warning in `eval_sites()`: study_id: C, site_number: a. pval == NA study_id: C, site_number: b. pval == NA study_id: C, site_number: c. pval == NA + Warning in `eval_sites()`: study_id: C, site_number: a, prob_low == NA diff --git a/vignettes/gsm_perf.Rmd b/vignettes/gsm_perf.Rmd index 61ff305..0de56d7 100644 --- a/vignettes/gsm_perf.Rmd +++ b/vignettes/gsm_perf.Rmd @@ -356,7 +356,7 @@ df_perf %>% ## Plot Performance Metrics -- {gsm} has better performance than {simaerep} whent the AE rate is fixed, while {simaerep} greatly outperforms {gsm} when the AE rate is flexible and mimics the AE rates encountered in real study data sets. +- {gsm} has better performance than {simaerep} when the AE rate is fixed, while {simaerep} greatly outperforms {gsm} when the AE rate is flexible and mimics the AE rates encountered in real study data sets. ```{r, plot, fig.width=10, fig.height = 12} From 2125c31b3d5d19a04189e7e2c1bafa19012166c9 Mon Sep 17 00:00:00 2001 From: Bjoern Koneswarakantha Date: Thu, 22 Feb 2024 14:47:41 +0100 Subject: [PATCH 5/7] fix linting --- R/0_imports.R | 9 --------- R/lint.R | 3 ++- 2 files changed, 2 insertions(+), 10 deletions(-) diff --git a/R/0_imports.R b/R/0_imports.R index a07e7fa..6a3cf13 100644 --- a/R/0_imports.R +++ b/R/0_imports.R @@ -1,12 +1,3 @@ -# satisfy lintr -# lintr falsely flags possibly_ecdf as unused variable -# using rlang::.data here causes error with furrr in sim_test_data_portfolio -# patnum, n_ae, visit - -if (getRversion() >= "2.15.1") { - utils::globalVariables(c("possibly_ecdf", "patnum", "n_ae", "visit")) -} - #' @importFrom progressr progressor #' @importFrom cowplot get_legend plot_grid ggdraw draw_label plot_grid plot_grid #' @importFrom cowplot ggdraw draw_label diff --git a/R/lint.R b/R/lint.R index d46482d..889ddf9 100644 --- a/R/lint.R +++ b/R/lint.R @@ -16,7 +16,8 @@ lint_package <- function(path = ".", ...) { linters = lintr::linters_with_defaults( line_length_linter = lintr::line_length_linter(120), trailing_whitespace_linter = NULL, - cyclocomp_linter = lintr::cyclocomp_linter(25) + cyclocomp_linter = lintr::cyclocomp_linter(25), + indentation_linter = NULL ), exclusions = list("inst/logo/logo.R", "tests/spelling.R", "vignettes"), ...) From aa19c67376eb9f39f2b37d3471fb540c22ff565f Mon Sep 17 00:00:00 2001 From: Bjoern Koneswarakantha Date: Thu, 22 Feb 2024 15:29:34 +0100 Subject: [PATCH 6/7] more lint fixes --- R/simaerep.R | 2 +- R/simulate_test_data.R | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/R/simaerep.R b/R/simaerep.R index 27e2381..a2d1271 100644 --- a/R/simaerep.R +++ b/R/simaerep.R @@ -975,7 +975,7 @@ sim_after_prep <- function(df_sim_prep, get_pat_pool_config <- function(df_visit, df_site, min_n_pat_with_med75 = 1) { # site_config are the number of sites with their individual visit_med75 and n_pat_with_med75 - df_site_config <- select(df_site,c( + df_site_config <- select(df_site, c( "study_id", "site_number", "visit_med75", diff --git a/R/simulate_test_data.R b/R/simulate_test_data.R index 9a23077..e9eb366 100644 --- a/R/simulate_test_data.R +++ b/R/simulate_test_data.R @@ -48,14 +48,14 @@ sim_test_data_study <- function(n_pat = 1000, if (! any(c(is.null(ae_rates), is.na(ae_rates)))) { if (is_ur) { - ae_rates <- ae_rates * (1-ur_rate) + ae_rates <- ae_rates * (1-ur_rate) # nolint } f_sample_ae <- function(max_visit) { # extrapolate missing ae rates by extending last rate fill <- rep(ae_rates[length(ae_rates)], max_visit) - fill[1: length(ae_rates)] <- ae_rates + fill[seq_along(ae_rates)] <- ae_rates ae_rates <- fill aes <- integer(0) @@ -606,7 +606,7 @@ sim_test_data_portfolio <- function(df_config, df_ae_rates = NULL, parallel = FA df_portf <- df_config_sim %>% unnest("sim") %>% - select(- c("n_pat" ,"ae_rates")) %>% + select(- c("n_pat", "ae_rates")) %>% group_by(.data$study_id) %>% mutate( # patnums need to be made site exclusive From ad77dec464eb3931189b8917b17c65f3246c18d9 Mon Sep 17 00:00:00 2001 From: Bjoern Koneswarakantha Date: Thu, 22 Feb 2024 15:41:49 +0100 Subject: [PATCH 7/7] another lint fix --- R/simulate_test_data.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/simulate_test_data.R b/R/simulate_test_data.R index e9eb366..e6dfcfc 100644 --- a/R/simulate_test_data.R +++ b/R/simulate_test_data.R @@ -74,7 +74,7 @@ sim_test_data_study <- function(n_pat = 1000, } else { if (is_ur) { - ae_per_visit_mean <- ae_per_visit_mean * (1-ur_rate) + ae_per_visit_mean <- ae_per_visit_mean * (1-ur_rate) # nolint } f_sample_ae <- function(max_visit) { @@ -824,7 +824,7 @@ get_portf_perf <- function(df_scen, stat = "prob_low_prob_ur", fpr = c(0.001, 0. } - stat_at_0 <- df_scen %>% #nolint + stat_at_0 <- df_scen %>% # nolint filter(.data$ur_rate == 0, .data$frac_pat_with_ur == 0) %>% pull(.data[[stat]])