Optimal Bayesian hierarchical model to accelerate the development of tissue-agnostic drugs and basket trials

TL;DR

This paper introduces the optimal Bayesian hierarchical model (OBHM) for tissue-agnostic drug trials, addressing prior sensitivity and error control issues to improve decision-making in biomarker-driven treatments.

Contribution

It proposes a utility-based prior selection method for BHM, enhancing error balance and reducing subjectivity, with extensions demonstrating superior performance in simulations.

Findings

OBHM balances type I and II errors effectively.

Extensions COBHM and AOBHM outperform existing methods.

Simulation shows improved operating characteristics.

Abstract

Tissue-agnostic trials enroll patients based on their genetic biomarkers, not tumor type, in an attempt to determine if a new drug can successfully treat disease conditions based on biomarkers. The Bayesian hierarchical model (BHM) provides an attractive approach to design phase II tissue-agnostic trials by allowing information borrowing across multiple disease types. In this article, we elucidate two intrinsic and inevitable issues that may limit the use of BHM to tissue-agnostic trials: sensitivity to the prior specification of the shrinkage parameter and the competing "interest" among disease types in increasing power and controlling type I error. To address these issues, we propose the optimal BHM (OBHM) approach. With OBHM, we first specify a flexible utility function to quantify the tradeoff between type I error and power across disease type based on the study objectives, and then we select the prior of the shrinkage parameter to optimize the utility function of clinical and regulatory interest. OBMH effectively balances type I and II errors, addresses the sensitivity of the prior selection, and reduces the "unwarranted" subjectivity in the prior selection. Simulation study shows that the resulting OBHM and its extensions, clustered OBHM (COBHM) and adaptive OBHM (AOBHM), have desirable operating characteristics, outperforming some existing methods with better balanced power and type I error control. Our method provides a systematic, rigorous way to apply BHM and solve the common problem of blindingly using a non-informative inverse-gamma prior (with a large variance) or priors arbitrarily chosen that may lead to pathological statistical properties.