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-Familywise Error Rate (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. (Source: http://cran.r-project.org/web/packages)
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References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Miecznikowski, Jeffrey C.; Gaile, Daniel P.: A novel characterization of the generalized family wise error rate using empirical null distributions (2014)
- Farcomeni, A.; Finos, L.: FDR control with pseudo-gatekeeping based on a possibly data driven order of the hypotheses (2013)
- He, Li; Sarkar, Sanat K.: On improving some adaptive BH procedures controlling the FDR under dependence (2013)
- Finos, L.; Farcomeni, A.: k-FWER control without (p )-value adjustment, with application to detection of genetic determinants of multiple sclerosis in Italian twins (2011)