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One challenge in exploratory association studies using observational data isthat the signals are potentially weak and the features have complex correlationstructures. False discovery rate (FDR) controlling procedures can provideimportant statistical guarantees for replicability in risk factoridentification in exploratory research. In the recently established NationalCOVID Collaborative Cohort (N3C), electronic health record (EHR) data on thesame set of candidate features are independently collected in multipledifferent sites, offering opportunities to identify signals by combininginformation from different sources. This paper presents a generalknockoff-based variable selection algorithm to identify mutual signals fromunions of group-level conditional independence tests with exact FDR controlguarantees under finite sample settings. This algorithm can work with generalregression settings, allowing heterogeneity of both the predictors and theoutcomes across multiple data sources. We demonstrate the performance of thismethod with extensive numerical studies and an application to the N3C data.

Controlling FDR in selecting group-level simultaneous signals from  multiple data sources with application to the National Covid Collaborative  Cohort data