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<html><head></head>
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</end><table border="0" cellpadding="0" cellspacing="8" width="100%">
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<img src="http://www.stat.sfu.ca/~mcneney/bski.gif" alt="snapshot" ,="" align="CENTER," width="300">
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<div align="center">
<b><h3> Brad McNeney </h3></b>
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<tbody><tr><td align="left"> Assistant Professor</td>
<td align="center"> </td>
<td align="left"> Office K10556</td>
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<a href="http://www.stat.sfu.ca/">Statistics and Actuarial Science</a> </td>
<td align="center"> </td>
<td align="left"> <a href="http://stat-db.stat.sfu.ca/statgen/">Statgen Working Group</a></td>
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<td align="left"> E-mail: <a href="mailto:[email protected]">[email protected]</a> </td>
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<a href="http://www.sfu.ca/"> Simon Fraser University </a> </td>
<td align="center"> </td>
<td align="left"> Burnaby, BC V5A 1S6</td>
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<tr><td align="left"> Tel: (604)291-4815</td>
<td align="center"> </td>
<td align="left"> Fax: (604)291-4368</td>
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<hr>
My research interests are in semiparametric statistical
models in genetic epidemiology.
The major research aim is improved inference for
genetic association studies, with emphasis on haplotype associations.
This aim stems largely from
statistical analysis issues related to a Swedish case-control
study of genetic associations with Type 1 diabetes on which I collaborate
with Dr. J. Graham.
We have developed a MySQL database and
web interface for easy data access, to facilitate interaction with
biomedical collaborators generating the data
and to simplify our own analyses for the project.
<p>
Data from case-control studies are often analysed by fitting
a logistic regression model to obtain odds-ratio parameter
estimates, as though the data were collected
prospectively. Prentice and Pyke (1979)
justified this approach to analysis. Their
result relies on a somewhat delicate argument. One of the key
assumptions is that the covariate distribution
is completely unspecified.
Motivated by the diabetes project I have been working with
J. Graham and graduate student J. Shin to develop maximum likelihood
estimators under an assumption of independence between a genetic
covariate and a non-genetic attribute.
I am also interested in more general
biased sampling designs, such as two-phase
or response-selective sampling.
One of our research goals
is to investigate response-selective
designs and parameter estimation for genetic association studies.
</p>
<p>
I also have interests in computational molecular biology through
research related to HIV vaccine development.
In a project with F. Seillier-Moiseiwitsch
(University of Maryland) and J. Graham (SFU), we have developed
a stepwise procedure for detecting
recombination breakpoints from sequence alignments.
The overall goal to help assess the number
and diversity of HIV quasispecies transmitted to an individual. The larger
the number of transmitted quasispecies, the more difficult the task of
developing an effective vaccine.
Accounting for recombination is an important consideration in this
research.
</p><h4>Selected publications</h4>
<p>
Burkett K, McNeney B, Graham J. A note on inference of trait
associations with SNP haplotypes and other attributes in generalized
linear models.
<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=15583426"><em>Human Heredity </em> 2004; <b>57</b>: 200-206.</a>
</p><p>
Graham J, McNeney B, Seillier-Moiseiwitsch F. Stepwise detection of
recombination breakpoints in sequence alignments.
<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=15388518">
<em>Bioinformatics </em> 2004;<b> Sep 23 [Epub ahead of print] </b></a>.
</p><p>
Breslow, NE, McNeney, B and Wellner, JA (2003). Large Sample Theory for Semiparametric Regression Models with Two-Phase, Outcome Dependent Sampling. <i>Ann Statist</i> <b>31</b>:1110 - 1139
</p><p>
Tsiatis B, Davidian M and McNeney B (2002).Multiple imputation methods for
testing treatment differences in survival distributions with missing cause
of failure. <i>Biometrika</i> <b>35</b>:1113-1121.
</p><p>
McNeney, B. and Wellner, J.A. (2000). Application of convolution theorems in semiparametric models with non-i.i.d data. <i>Journal of Statistical Planning and Inference</i> <b>91(2):</b> 441-480.
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