Data-driven hypothesis weighting increases detection power in multiple testing

• DOI: 10.1101/034330
• Publication Date: 2015-12-14T06:07:27Z
• Authors: Nikolaos Ignatiadis; Bernd Klaus; Judith Zaugg; Wolfgang Huber
• Keywords: Categorical variable; False Discovery Rate; Statistical power; Statistic; Null (SQL); Alternative hypothesis; Multiple comparisons problem

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