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k ‐FWER Control without p ‐value Adjustment, with Application to Detection of Genetic Determinants of Multiple Sclerosis in Italian Twins
Author(s) -
Finos L.,
Farcomeni A.
Publication year - 2011
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2010.01443.x
Subject(s) - nonparametric statistics , sample size determination , value (mathematics) , computer science , control (management) , parametric statistics , multiple comparisons problem , simple (philosophy) , false discovery rate , multiple sclerosis , statistics , mathematics , econometrics , medicine , artificial intelligence , biology , gene , genetics , philosophy , epistemology , psychiatry
Summary 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.