A Nonparametric Bayesian Basket Trial Design

TL;DR

This paper introduces a nonparametric Bayesian adaptive trial design that identifies patient subpopulations most likely to benefit from targeted therapies across various cancer types, accommodating complex data and missing information.

Contribution

It proposes a novel Bayesian trial framework that handles variable covariates, missing data, and high-order interactions for effective patient stratification and treatment allocation.

Findings

Demonstrated effectiveness through extensive simulations.

Improved patient subpopulation identification.

Adaptive treatment allocation enhances trial efficiency.

Abstract

Targeted therapies on the basis of genomic aberrations analysis of the tumor have shown promising results in cancer prognosis and treatment. Regardless of tumor type, trials that match patients to targeted therapies for their particular genomic aberrations have become a mainstream direction of therapeutic management of patients with cancer. Therefore, finding the subpopulation of patients who can most benefit from an aberration-specific targeted therapy across multiple cancer types is important. We propose an adaptive Bayesian clinical trial design for patient allocation and subpopulation identification. We start with a decision theoretic approach, including a utility function and a probability model across all possible subpopulation models. The main features of the proposed design and population finding methods are that we allow for variable sets of covariates to be recorded by different patients, adjust for missing data, allow high order interactions of covariates, and the adaptive allocation of each patient to treatment arms using the posterior predictive probability of which arm is best for each patient. The new method is demonstrated via extensive simulation studies.