Robust Bayesian hierarchical models for basket trials enabling joint evaluation of toxicity and efficacy

TL;DR

This paper introduces two Bayesian hierarchical models for basket trials that jointly analyze toxicity and efficacy, accounting for heterogeneity and correlation across subgroups, improving robustness and statistical power.

Contribution

The paper presents novel Bayesian hierarchical models enabling joint toxicity and efficacy analysis with flexible exchangeability assumptions and correlation modeling in basket trials.

Findings

Models outperform standard approaches in simulations.

Higher power when subgroup effects are exchangeable.

Small error rates with correlated toxicity and efficacy effects.

Abstract

Basket trials have gained increasing attention for their efficiency, as multiple patient subgroups are evaluated simultaneously. Conducted basket trials focus primarily on establishing the early efficacy of a treatment, yet continued monitoring of toxicity is essential. In this paper, we propose two Bayesian hierarchical models that enable bivariate analyses of toxicity and efficacy, while accounting for heterogeneity present in the treatment effects across patient subgroups. Specifically, one assumes the subgroup-specific toxicity and efficacy treatment effects, as a parameter vector, can be exchangeable or non-exchangeable; the other allows either the toxicity or efficacy parameters specific to the subgroups, to be exchangeable or non-exchangeable. The bivariate exchangeability and non-exchangeability distributions introduce a correlation parameter between treatment effects, while we stipulate a zero correlation when only toxicity or efficacy parameters are exchangeable. Simulation results show that our models perform robustly under different scenarios compared to the standard Bayesian hierarchical model and the stand-alone analyses, especially in producing higher power when the subgroup-specific effects are exchangeable in toxicity or efficacy only. When considerable correlation between the toxicity and efficacy effects exists, our methodology gives small error rates and greater power than alternatives that analyse toxicity and efficacy by parts.