BOP2-DC: Bayesian optimal phase II designs with dual-criterion decision making

TL;DR

BOP2-DC is a Bayesian phase II trial design that integrates statistical significance and clinical relevance for more nuanced decision making, optimizing trial outcomes across various endpoint types.

Contribution

It introduces a flexible Bayesian framework with dual-criterion decision making, enhancing phase II trial design beyond traditional significance testing.

Findings

Yields favorable operating characteristics in simulations

Accommodates multiple endpoint types

Provides a freely available implementation software

Abstract

The conventional phase II trial design paradigm is to make the go/no-go decision based on the hypothesis testing framework. Statistical significance itself alone, however, may not be sufficient to establish that the drug is clinically effective enough to warrant confirmatory phase III trials. We propose the Bayesian optimal phase II trial design with dual-criterion decision making (BOP2-DC), which incorporates both statistical significance and clinical relevance into decision making. Based on the posterior probability that the treatment effect reaches the lower reference value (statistical significance) and the clinically meaningful value (clinical significance), BOP2-DC allows for go/consider/no-go decisions, rather than a binary go/no-go decision, and it is optimized to maximize the probability of a go decision when the treatment is effective or minimize the sample size when the treatment is futile. BOP2-DC is highly flexible and accommodates various types of endpoints, including binary, continuous, time-to-event, multiple, and co-primary endpoints, in single-arm and randomized trials. Simulation studies show that the BOP2-DC design yields desirable operating characteristics. The software to implement BOP2-DC is freely available at \url{www.trialdesign.org}.