Probability of Study Success (PrSS) Evaluation Based on Multiple Endpoints in Late Phase Oncology Drug Development

Abstract

Phase 2 Oncology clinical trials are increasingly designed with multiple primary endpoints, such as progression-free survival (PFS) and overall survival (OS). While this gives a clearer picture of treatment benefit than trials with a single endpoint, it complicates the decision to begin a registration (phase 3) trial following a phase 2 trial. Methods for calculating the probability of study success (PrSS) assume the distribution of OS is well understood, but rarely consider the case of multiple primary endpoints. We introduce the BAMBOO method (BAyesian Model that Brings phase 2 cOmposite endpOints) to address this gap. We propose modeling the phase 2 log hazard ratios for PFS and OS as a bivariate normal random variable, with the unknown correlation parameter estimated with a meta-analysis or via simulation. The PrSS of a phase 3 study with multiple primary endpoints study can then be obtained using the posterior predictive distribution of this bivariate normal random variable. We provide methods to include additional surrogate endpoints, such as objective response rate, by extending the model to a multivariate normal of arbitrary dimension. Simulation results suggest the proposed method can better address the known prior-data conflict issue comparing to existing approaches, while borrowing historical data with caution is generally recommended under high discrepancy and small sample size. Finally, we provide an example of how BAMBOO can aid in planning a phase 3 trial.

KEYWORDS:

• Bayesian
• Go/no go
• OS
• PFS

Supplementary Materials

The Supplementary Materials file contains the derivation of the priors, empirical approaches for checking prior-data conflict, design parameters of the proposed framework with sensitivity analysis, simulation results under additional scenarios, and a post-hoc Bayesian meta-analysis model to bridge PFS/ORR critical success factor cutoffs.

Acknowledgments

The authors are grateful to Eli Kravitz and two anonymous reviewers for their helpful comments and suggestions that led to a significant improvement of the work.

Disclosure Statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) reported there is no funding associated with the work featured in this article.