Hierarchical Bayes Approach to Estimate the Treatment Effect for Randomized Controlled Trials.

ABSTRACT: One important issue in Bayesian estimation is the determination of an effective informative prior. In hierarchical Bayes models, the uncertainty of hyperparameters in a prior can be further modeled via their own priors, namely, hyper priors. This study introduces a framework to construct hyper priors for both the mean and the variance hyperparameters for estimating the treatment effect in a two-group randomized controlled trial. Assuming a random sample of treatment effect sizes is obtained from past studies, the hyper priors can be constructed based on the sampling distributions of the effect size mean and precision. The performance of the hierarchical Bayes approach was compared with the empirical Bayes approach (hyperparameters are fixed values or point estimates) and the ordinary least

SUBMITTER: Liang X 

PROVIDER: S-EPMC7565114 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature