k‐FWER Control without p ‐value Adjustment,with Application to Detection of Genetic Determinants of Multiple Sclerosis in Italian Twins |
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| Authors: | L. Finos A. Farcomeni |
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| Affiliation: | 1. Department of Statistical Sciences, University of Padua, Padua, Italy and Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, The Netherlands;2. Department of Hygiene and Public Health Sapienza—University of Rome, Rome, Italy |
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| 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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| Keywords: | Data‐driven order Gene discovery k‐Familywise error rate Multiple sclerosis Multiple testing |
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