A Flexible Multi-Metric Bayesian Framework for Decision-Making in Phase II Multi-Arm Multi-Stage Studies

TL;DR

This paper introduces a Bayesian multi-metric framework for phase II multi-arm multi-stage clinical trials, enabling more efficient and confident interim decisions with small sample sizes by integrating multiple evidence metrics.

Contribution

It presents a novel multi-metric Bayesian approach that incorporates various evidence types for arm ranking and comparison, improving decision-making in early-phase trials.

Findings

Framework achieves sufficient confidence with as few as 30 patients per arm.

Handles moderate correlation between intermediate and primary outcomes.

Well-received by research partners for practical application.

Abstract

We propose a multi-metric flexible Bayesian framework to support efficient interim decision-making in multi-arm multi-stage phase II clinical trials. Multi-arm multi-stage phase II studies increase the efficiency of drug development, but early decisions regarding the futility or desirability of a given arm carry considerable risk since sample sizes are often low and follow-up periods may be short. Further, since intermediate outcomes based on biomarkers of treatment response are rarely perfect surrogates for the primary outcome and different trial stakeholders may have different levels of risk tolerance, a single hypothesis test is insufficient for comprehensively summarizing the state of the collected evidence. We present a Bayesian framework comprised of multiple metrics based on point estimates, uncertainty, and evidence towards desired thresholds (a Target Product Profile) for 1) ranking of arms and 2) comparison of each arm against an internal control. Using a large public-private partnership targeting novel TB arms as a motivating example, we find via simulation study that our multi-metric framework provides sufficient confidence for decision-making with sample sizes as low as 30 patients per arm, even when intermediate outcomes have only moderate correlation with the primary outcome. Our reframing of trial design and the decision-making procedure has been well-received by research partners and is a practical approach to more efficient assessment of novel therapeutics.