k-FWER Control without p -value Adjustment, with Application to Detection of Genetic Determinants of Multiple Sclerosis in Italian Twins

Authors

  • L. Finos,

    Corresponding author
    1. Department of Statistical Sciences, University of Padua, Padua, Italy and Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, The Netherlands
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  • A. Farcomeni

    Corresponding author
    1. Department of Hygiene and Public Health Sapienza—University of Rome, Rome, Italy
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email:livio@stat.unipd.it

email:alessio.farcomeni@uniroma1.it

Abstract

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.

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