Latent Class Mixture Models of Treatment Effect Heterogeneity

Abstract

We provide a general Bayesian framework for modeling treatment effect heterogeneity in experiments with non-categorical outcomes. Our modeling approach incorporates latent class mixture components to capture discrete heterogeneity and regression interaction terms to capture continuous heterogeneity. Flexible error distributions allow robust posterior inference on parameters of interest. Hierarchical shrinkage priors on relevant parameters address multiple comparisons concerns. Leave-one-out cross validation estimates of expected posterior predictive density obtained through importance sampling, together with posterior predictive checks, provide a convenient method for model selection and evaluation. We apply our approach to a clinical trial comparing two HIV treatments and to an instrumental variable analysis of a natural experiment on the effect of Medicaid enrollment on emergency department utilization.