Mixture of Finite Mixtures Model for Basket Trial

TL;DR

This paper introduces a novel two-step clustering and Bayesian hierarchical modeling approach for basket trials, balancing pooled and stratified analysis to improve treatment effect estimation in oncology studies.

Contribution

It proposes a mixture of finite mixtures model to identify clusters of cohorts with similar effects, enhancing analysis accuracy in basket trials.

Findings

The method accurately estimates the number of clusters in simulations.

Application to Vemurafenib data demonstrates practical utility.

Improves treatment effect estimation by balancing pooling and stratification.

Abstract

With the recent paradigm shift from cytotoxic drugs to new generation of target therapy and immuno-oncology therapy during oncology drug developments, patients with various cancer (sub)types may be eligible to participate in a basket trial if they have the same molecular target. Bayesian hierarchical modeling (BHM) are widely used in basket trial data analysis, where they adaptively borrow information among different cohorts (subtypes) rather than fully pool the data together or doing stratified analysis based on each cohort. Those approaches, however, may have the risk of over shrinkage estimation because of the invalidated exchangeable assumption. We propose a two-step procedure to find the balance between pooled and stratified analysis. In the first step, we treat it as a clustering problem by grouping cohorts into clusters that share the similar treatment effect. In the second step, we use shrinkage estimator from BHM to estimate treatment effects for cohorts within each cluster under exchangeable assumption. For clustering part, we adapt the mixture of finite mixtures (MFM) approach to have consistent estimate of the number of clusters. We investigate the performance of our proposed method in simulation studies and apply this method to Vemurafenib basket trial data analysis.