Refactor methods by passing table metadata as attributes

[jdblischak]

This is an alternative approach to #482 to refactor the classes/methods as discussed in #457

This PR refactors the fixed_design_X() functions to define their own design_display, title, and footnote as attributes, which are then used by summary(), as_gt(), and as_rtf(). This idea was proposed by @yihui, which I described in #482 (comment)

By using the above strategy, I was able to make the following improvements to the classes/methods:

• summary.fixed_design() no longer adds a method class to its output object (eg "ahr") since this is no longer necessary. This was only used to constructed the table metadata, which is now already defined in the attributes of the object produced the respective fixed_design_X() function
• summary.fixed_design() returns an object of class "fixed_design_summary" to distinguish it from the output of a fixed_design_X() function (with class "fixed_design")

Below is example code to test out the changes in this PR:

devtools::load_all(".")

# ahr
x <- fixed_design_ahr(
  alpha = .025,
  enroll_rate = define_enroll_rate(duration = 18, rate = 20),
  fail_rate = define_fail_rate(
    duration = c(4, 100),
    fail_rate = log(2) / 12,
    hr = c(1, .6),
    dropout_rate = .001
  ),
  study_duration = 36
)
attributes(x)
## $names
## [1] "input"       "enroll_rate" "fail_rate"   "analysis"    "design"
##
## $design_display
## [1] "Average hazard ratio"
##
## $title
## [1] "Fixed Design under AHR Method"
##
## $footnote
## [1] "Power computed with average hazard ratio method."
##
## $class
## [1] "fixed_design" "list"
xsum <- summary(x)
attributes(xsum)
## $row.names
## [1] 1
##
## $names
## [1] "Design" "N"      "Events" "Time"   "Bound"  "alpha"  "Power"
##
## $class
## [1] "fixed_design_summary" "tbl_df"               "tbl"                  "data.frame"
##
## $title
## [1] "Fixed Design under AHR Method"
##
## $footnote
## [1] "Power computed with average hazard ratio method."
as_gt(xsum)
as_gt(xsum, title = "Custom title", footnote = "Custom footnote")
as_rtf(xsum, file = "refactor.rtf")
as_rtf(xsum, file = "refactor.rtf", title = "Custom title", footnote = "Custom footnote")

# fh
x <- fixed_design_fh(
  alpha = .025,
  enroll_rate = define_enroll_rate(duration = 18, rate = 20),
  fail_rate = define_fail_rate(
    duration = c(4, 100),
    fail_rate = log(2) / 12,
    hr = c(1, .6),
    dropout_rate = .001
  ),
  study_duration = 36,
  rho = 1, gamma = 1
)
attributes(x)
xsum <- summary(x)
attributes(xsum)
as_gt(xsum)
as_rtf(xsum, file = "refactor.rtf")

# mb
x <- fixed_design_mb(
  alpha = .025,
  enroll_rate = define_enroll_rate(duration = 18, rate = 20),
  fail_rate = define_fail_rate(
    duration = c(4, 100),
    fail_rate = log(2) / 12,
    hr = c(1, .6),
    dropout_rate = .001
  ),
  study_duration = 36,
  tau = 4,
  w_max = 2
)
attributes(x)
xsum <- summary(x)
attributes(xsum)
as_gt(xsum)
as_rtf(xsum, file = "refactor.rtf")

# lf
x <- fixed_design_lf(
  alpha = .025, power = .9,
  enroll_rate = define_enroll_rate(duration = 18, rate = 1),
  fail_rate = define_fail_rate(
    duration = 100,
    fail_rate = log(2) / 12,
    hr = .7,
    dropout_rate = .001
  ),
  study_duration = 36
)
attributes(x)
xsum <- summary(x)
attributes(xsum)
as_gt(xsum)
as_rtf(xsum, file = "refactor.rtf")

# rd
x <- fixed_design_rd(
  alpha = 0.025, power = 0.9, p_c = .15, p_e = .1,
  rd0 = 0, ratio = 1
)
attributes(x)
xsum <- summary(x)
attributes(xsum)
as_gt(xsum)
as_rtf(xsum, file = "refactor.rtf")

# maxcombo
x <- fixed_design_maxcombo(
  alpha = .025,
  enroll_rate = define_enroll_rate(duration = 18, rate = 20),
  fail_rate = define_fail_rate(
    duration = c(4, 100),
    fail_rate = log(2) / 12,
    hr = c(1, .6),
    dropout_rate = .001
  ),
  study_duration = 36,
  rho = c(0, 0.5), gamma = c(0, 0), tau = c(-1, -1)
)
attributes(x)
xsum <- summary(x)
attributes(xsum)
as_gt(xsum)
as_rtf(xsum, file = "refactor.rtf")

# milestone
x <- fixed_design_milestone(
  alpha = .025,
  enroll_rate = define_enroll_rate(duration = 18, rate = 20),
  fail_rate = define_fail_rate(
    duration = 100,
    fail_rate = log(2) / 12,
    hr = .7,
    dropout_rate = .001
  ),
  study_duration = 36,
  tau = 18
)
attributes(x)
xsum <- summary(x)
attributes(xsum)
as_gt(xsum)
as_rtf(xsum, file = "refactor.rtf")

# rmst
x <- fixed_design_rmst(
  alpha = .025,
  enroll_rate = define_enroll_rate(duration = 18, rate = 20),
  fail_rate = define_fail_rate(
    duration = 100,
    fail_rate = log(2) / 12,
    hr = .7,
    dropout_rate = .001
  ),
  study_duration = 36,
  tau = 18
)
attributes(x)
xsum <- summary(x)
attributes(xsum)
as_gt(xsum)
as_rtf(xsum, file = "refactor.rtf")

[nanxstats]

This looks clear to me. I apologize for the complexity - if we could have written this in classical OOP, things would have been much more straightforward. This is one of the few things where I think Python gets it right but base R didn't.