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Adaptively weighted association statistics
Author(s) -
LeBlanc Michael,
Kooperberg Charles
Publication year - 2009
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.20397
Subject(s) - statistics , type i and type ii errors , statistic , weighting , test statistic , statistical hypothesis testing , multiple comparisons problem , monte carlo method , sample size determination , summary statistics , computer science , mathematics , econometrics , medicine , radiology
We investigate methods for testing gene‐disease outcome associations in situations where the genetic relationship potentially varies among subjects with differing environmental or clinical attributes. We propose a strategy which modestly increases multiple testing by evaluating weighted test statistics which focus (or enrich) association tests within subgroups and use a Monte‐Carlo method, based on simulating from the approximate large sample distribution of the statistics, to control type 1 error. We also introduce a stage‐wise calculated test statistic which allows more complex weighting on multiple environmental variables. Results from simulation studies confirm improved power of the proposed approaches compared to marginal testing in many situations. Genet. Epidemiol . 33:442–452, 2009. © 2009 Wiley‐Liss, Inc.