Bayesian Estimation of Population Average Causal Effects from a Collection of Trials

Music City Center 

We propose two Bayesian mixed effects models, one linear and one linear spline, to estimate the average effect of a binary treatment on a target population via one-stage meta-analysis. In an extension of previous work in a frequentist setting, we aim to combine information from a collection of randomized trials to identify the average treatment effect (ATE) on a separate, non-study target population, by allowing study-level random effects to account for variations in outcome due to differences in studies. We examine, with simulation studies, several situations in which weight-based estimators and/or nonparametric machine learning methods face challenges in estimating a population ATE, and highlight the advantages of our parametric, outcome-based estimators.

meta analysis

generalizability

mixed effects models 

Health Policy Statistics Section