Powerful Partial Conjunction Hypothesis Testing via Conditioning

TL;DR

This paper introduces the conditional PCH (cPCH) test, a new method that improves power and maintains error control in partial conjunction hypothesis testing by conditioning on order statistics of p-values, especially in low signal scenarios.

Contribution

The paper proposes the cPCH test, a novel approach that reduces conservativeness and enhances power in PCH testing by conditioning on order statistics, applicable even under model misspecification.

Findings

cPCH produces nearly uniform p-values under the null

cPCH outperforms existing PCH tests in low signal settings

cPCH maintains Type I error control and benefits from side information

Abstract

A Partial Conjunction Hypothesis (PCH) test combines information across a set of base hypotheses to determine whether some subset is non-null. PCH tests arise in a diverse array of fields, but standard PCH testing methods can be highly conservative, leading to low power especially in low signal settings commonly encountered in applications. In this paper, we introduce the conditional PCH (cPCH) test, a new method for testing a single PCH that directly corrects the conservativeness of standard approaches by conditioning on certain order statistics of the base p-values. Under distributional assumptions commonly encountered in PCH testing, the cPCH test is valid and produces nearly uniformly distributed p-values under the null (i.e., cPCH p-values are only very slightly conservative). We demonstrate that the cPCH test matches or outperforms existing single PCH tests with particular power gains in low signal settings, maintains Type I error control even under model misspecification, and can be used to outperform state-of-the-art multiple PCH testing procedures in certain settings, particularly when side information is present. Finally, we illustrate an application of the cPCH test through a replicability analysis across DNA microarray studies.