k-FWER Control without p -value Adjustment, with Application to Detection of Genetic Determinants of Multiple Sclerosis in Italian Twins
Article first published online: 1 JUN 2010
© 2010, The International Biometric Society
Volume 67, Issue 1, pages 174–181, March 2011
How to Cite
Finos, L. and Farcomeni, A. (2011), k-FWER Control without p -value Adjustment, with Application to Detection of Genetic Determinants of Multiple Sclerosis in Italian Twins. Biometrics, 67: 174–181. doi: 10.1111/j.1541-0420.2010.01443.x
- Issue published online: 14 MAR 2011
- Article first published online: 1 JUN 2010
- Received April 2009. Revised March 2010. Accepted March 2010.
- Data-driven order;
- Gene discovery;
- k-Familywise error rate;
- Multiple sclerosis;
- Multiple testing
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.