Affordable Access

deepdyve-link
Publisher Website

Gaining power in multiple testing of interval hypotheses via conditionalization.

Authors
  • Ellis, Jules L1
  • Pecanka, Jakub2
  • Goeman, Jelle J2
  • 1 Behavioral Science Institute, Radboud University Nijmegen, Postbus 9104, 6500 HE, Nijmegen, The Netherlands. , (Netherlands)
  • 2 Biomedical Data Sciences, Leiden University Medical Center, Postbus 9600, 2300 RC, Leiden, The Netherlands. , (Netherlands)
Type
Published Article
Journal
Biostatistics (Oxford, England)
Publication Date
Apr 01, 2020
Volume
21
Issue
2
Identifiers
DOI: 10.1093/biostatistics/kxy042
PMID: 30247521
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

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. © The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected]

Report this publication

Statistics

Seen <100 times