A Bayesian Treatment Selection Design for Phase II Randomised Cancer Clinical Trials

TL;DR

This paper introduces a Bayesian design for Phase II cancer trials that improves treatment selection by incorporating prior knowledge, updating beliefs with data, and providing flexible decision rules, supported by simulations and a user-friendly R Shiny app.

Contribution

It proposes a novel Bayesian decision rule for binary outcomes in Phase II trials, including sample size methods and an R Shiny tool for practical implementation.

Findings

The Bayesian design effectively selects treatments with higher success probabilities.

Simulation studies show improved flexibility and efficiency over traditional methods.

The R Shiny app facilitates clinical adoption of the proposed methodology.

Abstract

It is crucial to design Phase II cancer clinical trials that balance the efficiency of treatment selection with clinical practicality. Sargent and Goldberg proposed a frequentist design that allow decision-making even when the primary endpoint is ambiguous. However, frequentist approaches rely on fixed thresholds and long-run frequency properties, which can limit flexibility in practical applications. In contrast, the Bayesian decision rule, based on posterior probabilities, enables transparent decision-making by incorporating prior knowledge and updating beliefs with new data, addressing some of the inherent limitations of frequentist designs. In this study, we propose a novel Bayesian design, allowing selection of a best-performing treatment. Specifically, concerning phase II clinical trials with a binary outcome, our decision rule employs posterior interval probability by integrating the joint distribution over all values, for which the 'success rate' of the bester-performing treatment is greater than that of the other(s). This design can then determine which a treatment should proceed to the next phase, given predefined decision thresholds. Furthermore, we propose two sample size determination methods to empower such treatment selection designs implemented in a Bayesian framework. Through simulation studies and real-data applications, we demonstrate how this approach can overcome challenges related to sample size constraints in randomised trials. In addition, we present a user-friendly R Shiny application, enabling clinicians to Bayesian designs. Both our methodology and the software application can advance the design and analysis of clinical trials for evaluating cancer treatments.