Gaining power in multiple testing of interval hypotheses via conditionalization

Ellis, J.L.; Pecanka, J.; Goeman, J.J. (2020)

Article / Letter to editor

In this article, we introduce a novel procedure for improving power of multiple testing procedures (MTPs) of interval hypotheses. When testing interval hypotheses the null hypothesis P-values tend to be stochastically larger than standard uniform if the true parameter is in the interior of the null hypothesis. The new procedure starts with a set of P-values and discards those with values above a certain pre-selected threshold, while the rest are corrected (scaled-up) by the value of the threshold. Subsequently, a chosen family-wise error rate (FWER) or false discovery rate MTP is applied to the set of corrected P-values only. We prove the general validity of this procedure under independence of P-values, and for the special case of the Bonferroni method, we formulate several sufficient conditions for the control of the FWER. It is demonstrated that this "filtering" of P-values can yield considerable gains of power.

• Conditionalized test
• False discovery rate
• Family-wise error rate
• Multiple testing
• One-sided tests
• Uniform conditional stochastic order

• All authors: Ellis, J.L.; Pecanka, J.; Goeman, J.J.
• Date: 2020-04-01
• Journal: Biostatistics
• Volume: 21
• Issue: 2
• Pages: E65 - E79
• DOI: doi:10.1093/biostatistics/kxy042
• Link: https://academic.oup.com/biostatistics/article/21/2/e65/5105993