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Hierarchical Bayesian estimation of subgroup effects in large healthcare policy evaluations (307889)

Keywords: Bayesian statistics, subgroup analysis, policy evaluation

Healthcare policymakers have always been aware that policy interventions do not affect all individuals equally. This has led to an increased interest in subgroup-specific impact estimates, so the policymakers can understand under what settings interventions are most effective in improving care, reducing adverse outcomes, and decreasing costs. Traditional approaches to subgroup analysis typically estimate a different model for each subgroup of interest, leading to increased noise, spurious statistical significance (type-I errors), and exaggerated significant estimates (type-M errors). We present a hierarchical Bayesian model that estimates all subgroup-specific impacts simultaneously, borrowing strength across subgroups as appropriate. We demonstrate how to set up hierarchical priors that are suitable to reflect the structure of the data and the effects of interest. We also address several practical challenges that come up when the approach is applied to data from large healthcare policy evaluations, which routinely include millions of records. Our approach is motivated by and applied to the evaluation of a large CMS Model Test of a healthcare service delivery intervention.