Use of Bayesian approaches in oncology clinical trials: A cross-sectional analysis

Bayesian approaches may improve the efficiency of trials and accelerate decision-making, but reluctance to depart from traditional frequentist statistics may limit their use. Because oncology trials generally involve severe conditions with no or limited therapeutic options, they are well-suited to applying Bayesian methodologies and are perceived as using these methods often in early phases.

In this study, we aim to describe the use of Bayesian methods and designs in oncology clinical trials in the last 20 years.

A cross-sectional observational study was conducted to identify oncology clinical trials using Bayesian approaches registered in clinicaltrials.gov between 2004 and 2024. Trials were searched in clinicaltrials.gov, PubMed, and through manual search of cross-references.

Bayesian trials were retrieved, and their main characteristics were extracted using R and verified manually. Between 2004 and 2024, 384,298 trials were registered in clinicaltrials.gov; we identified 84,850 oncology clinical trials (22%), of which 640 (0.75%) used Bayesian approaches. The adoption of Bayesian trials increased significantly after 2011, but while half of all Bayesian studies started in the last 5 years, this paralleled the overall increase in oncology research rather than an increase in the proportion of Bayesian trials. The majority of Bayesian trials were phase 1 and phase 2 studies, and two-thirds of Bayesian trials with efficacy objectives had single-arm designs, often utilizing binary endpoints, such as overall response, as the primary measure.

The uptake of Bayesian methods in oncology clinical trials has flattened and is still scarce, and is mostly applied to the analysis of treatment efficacy in single-arm trials with binary endpoints. There is room for further uptake and use of their potential advantages in settings with small populations and severe conditions with unmet needs.