Compound p-Value Statistics for Multiple Testing Procedures

TL;DR

This paper introduces compound p-value statistics that utilize all available data, improving the power of multiple testing procedures while maintaining control over false positives, demonstrated through theoretical analysis, simulations, and real data application.

Contribution

It develops a new class of compound p-value statistics that depend on all data and satisfy independence and uniformity under nulls, enhancing testing power.

Findings

Compound p-values increase detection of false nulls.

Procedures maintain control of type I error rate.

Application to microarray data shows improved power.

Abstract

Many multiple testing procedures make use of the p-values from the individual pairs of hypothesis tests, and are valid if the p-value statistics are independent and uniformly distributed under the null hypotheses. However, it has recently been shown that these types of multiple testing procedures are inefficient since such p-values do not depend upon all of the available data. This paper provides tools for constructing compound p-value statistics, which are those that depend upon all of the available data, but still satisfy the conditions of independence and uniformity under the null hypotheses. As an example, a class of compound p-value statistics for testing for location shifts is developed. It is demonstrated, both analytically and through simulations, that multiple testing procedures tend to reject more false null hypotheses when applied to these compound p-values rather than the usual p-values, and at the same time still guarantee the desired type I error rate control. The compound p-values, in conjunction with two different multiple testing methods, are used to analyze a real microarray data set. Applying either multiple testing method to the compound p-values, instead of the usual p-values, enhances their powers.