Data-driven hypothesis weighting increases detection power in multiple testing

Abstract

Hypothesis weighting is a powerful approach for improving the power of data analyses that employ multiple testing. However, in general it is not evident how to choose the weights. We describe IHW, a method for data-driven hypothesis weighting that makes use of informative covariates that are independent of the test statistic under the null, but informative of each tests power or prior probability of the null hypothesis. Covariates can be continuous or categorical and need not fulfill any particular assumptions. The method increases statistical power in applications while controlling the false discovery rate (FDR) and produces additional insight by revealing the covariate-weight relationship. Independent hypothesis weighting is a practical approach to discovery of associations in large datasets.