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Comparative meta-analyses of patient groups by integrating multipleobservational studies rely on estimated propensity scores (PSs) to mitigateconfounder imbalances. However, PS estimation grapples with the theoretical andpractical challenges posed by high-dimensional confounders. Motivated by anintegrative analysis of breast cancer patients across seven medical centers,this paper tackles the challenges associated with integrating multipleobservational datasets and offering nationally interpretable results. Theproposed inferential technique, called Bayesian Motif Submatrices forConfounders (B-MSMC), addresses the curse of dimensionality by a hybrid ofBayesian and frequentist approaches. B-MSMC uses nonparametric Bayesian``Chinese restaurant" processes to eliminate redundancy in the high-dimensionalconfounders and discover latent motifs or lower-dimensional structure. Withthese motifs as potential predictors, standard regression techniques can beutilized to accurately infer the PSs and facilitate causal group comparisons.Simulations and meta-analysis of the motivating cancer investigationdemonstrate the efficacy of our proposal in high-dimensional causal inferenceby integrating multiple observational studies; using different weightingmethods, we apply the B-MSMC approach to efficiently address confounding whenintegrating observational health studies with high-dimensional confounders.

Causal Integration of Multiple Cancer Cohorts with High-Dimensional Confounders: Bayesian Propensity Score Estimation