Data-Dependent Weighted E-Value Aggregation for Fusion Learning

We propose a data-dependent weighted e-value aggregation framework for synthesizing discoveries across partially overlapping studies. The key idea is to convert within study p-value-based multiple testing results into e-values and aggregate them using data-dependent leave-one-out weights, thereby mitigating the power loss associated with naive averaging. We show that applying the e-Benjamini–Hochberg procedure to the aggregated e-values yields finite-sample control of the global false discovery rate under standard conditions. Simulation studies and real-data analyses demonstrate the effectiveness and practical advantages of the proposed methods.