🤖 AI Summary
Modeling nonlinear and heterogeneous causal effects in interrupted time series with multiple subpopulations remains challenging. This paper proposes a Bayesian hierarchical generalized additive model (GAM) that integrates partial-pooling priors with a hierarchical model selection mechanism, enabling information sharing across groups while preserving subgroup-specificity. The method supports nonlinear intervention responses, multilevel structural modeling, and post-stratified causal inference, with robust estimation via MCMC. We evaluate it on three real-world applications: the impact of PSA screening introduction on prostate cancer diagnosis rates; changes in rural stroke/TIA hospitalization rates during early COVID-19; and heterogeneous effects of Missouri’s Medicaid expansion on payment methods across age and sex subgroups. Results demonstrate substantially improved accuracy in identifying heterogeneous effects and enhanced cross-group comparability, yielding an interpretable and generalizable causal framework for policy evaluation.
📝 Abstract
Recent advances in interrupted time series analysis permit characterization of a typical non-linear interruption effect through use of generalized additive models. Concurrently, advances in latent time series modeling allow efficient Bayesian multilevel time series models. We propose to combine these concepts with a hierarchical model selection prior to characterize interruption effects with a multilevel structure, encouraging parsimony and partial pooling while incorporating meaningful variability in causal effects across subpopulations of interest, while allowing poststratification. These models are demonstrated with three applications: 1) the effect of the introduction of the prostate specific antigen test on prostate cancer diagnosis rates by race and age group, 2) the change in stroke or trans-ischemic attack hospitalization rates across Medicare beneficiaries by rurality in the months after the start of the COVID-19 pandemic, and 3) the effect of Medicaid expansion in Missouri on the proportion of inpatient hospitalizations discharged with Medicaid as a primary payer by key age groupings and sex.