Identifying Treatment Effect Heterogeneity with Bayesian Hierarchical Adjustable Random Partition

Music City Center 

In precision medicine, to identify sensitive population and direct treatment decisions, it is essential to investigate treatment effect heterogeneity by estimating subgroup-specific responses and identifying homogeneity patterns. However, conducting comparison between multiple interventions among potential subgroups is challenging. To increase power and precision, many Bayesian models partition subgroups into information-borrowing clusters, yet two challenges persist: capturing the uncertainty in partitioning configurations and adapting the strengths of borrowing. We propose a flexible Bayesian hierarchical model that relies on a mixture prior with variable number of components. For each intervention, the model partitions subgroups into mutually exclusive clusters, borrowing information within each cluster. To estimate the posterior distribution, we use a reversible jump MCMC approach that explores different partitions while adjusting borrowing strength based on within-cluster variability. We also introduce a Bayesian adaptive enrichment design to merge equivalent subgroups, enrich responsive subgroups and terminate futile arms, improving efficiency and flexibility.

Precision Medicine

Bayesian Adaptive Trials

Bayesian Hierarchical Model

Finite Mixture Model

Reversible jump Markov Chain Monte Carlo

Random Partition 

International Society for Bayesian Analysis (ISBA)