A1514
Title: Bayesian nonparametric sensitivity analysis of multiple test procedures under dependence
Authors: George Karabatsos - University of Illinois-Chicago (United States) [presenting]
Abstract: A sensitivity analysis method for Multiple Testing Procedures (MTPs) based on marginal p-values is introduced. The method is based on the Dirichlet process (DP) prior distribution, which is specified to support the entire space of MTPs, where each MTP controls either the family-wise error rate (FWER) or the false discovery rate (FDR) under arbitrary dependence among p-values. This DP MTP sensitivity analysis method provides uncertainty quantification for MTPs, by accounting for uncertainty in the selection of such MTPs and their respective threshold decisions regarding which number of smallest p-values are significant discoveries, from a given set of null hypothesis tested, while measuring each p-value's probability of significance over the DP prior predictive distribution of this space of all MTPs, and reducing the possible conservativeness of using only one such MTP for multiple testing. The DP MTP sensitivity analysis method is illustrated through the analysis of over 28,000 p-values arising from hypothesis tests performed on a 2022 dataset of a representative sample of 3 million U.S. high school students, observed across 239 variables. They include tests which, respectively, relate variables about the disruption caused by school closures during the COVID-19 pandemic, with various mathematical cognition, academic achievement, and student background variables. A new R package, bnpMTP, can be used to implement this sensitivity analysis method.
