Hierarchical Bayesian Approach to Estimate Interventional Effect in Randomized Control Trials

• In Event: 28.030 - Analysis in Experimental Designs and Quasi-Experimental Designs With Propensity Score Matching

Abstract

We propose a framework to construct hyper priors for both the mean and variance hyperparameters for estimating interventional effects in a two-group randomized control trial. One important issue in Bayesian estimation is the determination of an effective informative prior, especially with small sample sizes. With hierarchical Bayesian modeling, the uncertainty of hyperparameters of a prior can be further modeled via their own priors, namely, hyper priors. The performance of hierarchical Bayesian models was compared with empirical Bayesian models where hyperparameters were constants. In a preliminary analysis, the hierarchical Bayesian approach showed promising improvement on posterior inferences compared to the empirical Bayesian approach.

Authors

• Xinya Liang, University of Arkansas

• Akihito Kamata, Southern Methodist University

• Gozde Sirganci, Bozok University

• Ji Li