Randomized -values for multiple testing and their application in replicability analysis

We are concerned with testing replicability hypotheses for many endpoints simultaneously. This constitutes a multiple test problem with composite null hypotheses. Traditional $$ -values, which are computed under least favorable parameter configurations (LFCs), are over-conservative in the case of composite null hypotheses. As demonstrated in prior work, this poses severe challenges in the multiple testing context, especially when one goal of the statistical analysis is to estimate the proportion $$ of true null hypotheses. Randomized $$ -values have been proposed to remedy this issue. In the present work, we discuss the application of randomized $$ -values in replicability analysis. In particular, we introduce a general class of statistical models for which valid, randomized $$ -values can be calculated easily. By means of computer simulations, we demonstrate that their usage typically leads to a much more accurate estimation of $$ than the LFC-based approach. Finally, we apply our proposed methodology to a real data example from genomics.