k-FWER control without p -value adjustment, with application to detection of genetic determinants of multiple sclerosis in italian twins.
We show a novel approach for k-FWER control which does not involve any correction, but only testing the hypotheses along a (possibly data-driven) order until a suitable number of p-values are found above the uncorrected α level. p-values can arise from any linear model in a parametric or nonparametric setting. The approach is not only very simple and computationally undemanding, but also the data-driven order enhances power when the sample size is small (and also when k and/or the number of tests is large). We illustrate the method on an original study about gene discovery in multiple sclerosis, in which were involved a small number of couples of twins, discordant by disease. The methods are implemented in an R package (someKfwer), freely available on CRAN.
Department of Statistical Sciences, University of Padua, Padua, Italy and Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, The Netherlands. livio@stat.unipd.it
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D000465:Algorithms; D005260:Female; D005819:Genetic Markers / Q000235:genetics*; D020022:Genetic Predisposition to Disease / Q000453:epidemiology* / Q000235:genetics*; D006801:Humans; D007558:Italy / Q000453:epidemiology; D008297:Male; D009103:Multiple Sclerosis / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D014427:Twins / Q000235:genetics*
D005819:Genetic Markers