Group Competition Test: A P-value-free False Discovery Rate Control Framework for Grouped Hypotheses

Abstract

Grouped multiple testing arises in many high-dimensional applications, where hypotheses may differ across groups and valid p-values are unavailable. Applying competition-based procedures separately within groups can inflate the global false discovery rate (FDR), while conservative corrections may substantially reduce power. We extend the competition test framework to the grouped setting and propose two procedures. The group competition (GC) test provides finite-sample FDR bounds under arbitrary inter-group dependence. For homogeneous groups, the required correction has logarithmic order in the number of groups, and this order is unavoidable for the GC test. The consistent group competition (CGC) filter instead couples group-specific thresholds through a single global normalization. Under the stated basic-independence conditions, it controls the FDR in finite samples without a correction that grows with the number of groups; under arbitrary inter-group dependence, the same guarantee is retained with a per-group correction that depends on the symmetry parameter of each group. We further develop a parameterized-threshold analysis to establish asymptotic guarantees and to characterize when the per-group correction can be dropped, so that a simple filter still controls the FDR approximately. Simulation studies show that the GC test and the CGC filter can improve power over pooled competition testing in heterogeneous settings, whereas the pooled test may remain more powerful under homogeneity. A peptide-spectrum matching benchmark involving post-translational modifications illustrates the practical relevance of the proposed methods.