BOP2-TE: Bayesian Optimal Phase 2 Design for Jointly Monitoring Efficacy and Toxicity with Application to Dose Optimization

TL;DR

BOP2-TE is a Bayesian phase 2 trial design that jointly monitors efficacy and toxicity, improving decision accuracy and safety in dose optimization through a Dirichlet-multinomial model and seamless phase I/II integration.

Contribution

It introduces BOP2-TE, a novel Bayesian design that controls error rates and optimizes power for efficacy and toxicity monitoring, with a user-friendly implementation.

Findings

BOP2-TE improves safety and efficacy in simulations.

It offers rigorous type I error control.

The design is adaptable for dose optimization and seamless phase I/II trials.

Abstract

We propose a Bayesian optimal phase 2 design for jointly monitoring efficacy and toxicity, referred to as BOP2-TE, to improve the operating characteristics of the BOP2 design proposed by Zhou et al. (2017). BOP2-TE utilizes a Dirichlet-multinomial model to jointly model the distribution of toxicity and efficacy endpoints, making go/no-go decisions based on the posterior probability of toxicity and futility. In comparison to the original BOP2 and other existing designs, BOP2-TE offers the advantage of providing rigorous type I error control in cases where the treatment is toxic and futile, effective but toxic, or safe but futile, while optimizing power when the treatment is effective and safe. As a result, BOP2-TE enhances trial safety and efficacy. We also explore the incorporation of BOP2-TE into multiple-dose randomized trials for dose optimization, and consider a seamless design that integrates phase I dose finding with phase II randomized dose optimization. BOP2-TE is user-friendly, as its decision boundary can be determined prior to the trial's onset. Simulations demonstrate that BOP2-TE possesses desirable operating characteristics. We have developed a user-friendly web application as part of the BOP2 app, which is freely available at www.trialdesign.org.