An efficient single-arm Bayesian adaptive trial algorithm to evaluate de-intensified oncologic treatment.

Zhong Y, Baskurt Z, Aminilari M, Seelisch J, Renfro LA, Castellino SM, Xu W, Hodgson D. An efficient single-arm Bayesian adaptive trial algorithm to evaluate de-intensified oncologic treatment. Trials. 2025 Dec 10;27(1):34. doi: 10.1186/s13063-025-09315-6. PMID: 41372997; PMCID: PMC12801998.

In clinical trials, evaluating de-intensified oncologic treatment strategies can help reduce treatment-related toxicities while preserving patients’ quality of life. However, de-intensification is typically evaluated in cancers with a low relapse rate, and if the cancer type is uncommon, a randomized trial may require an impractically extended period to accumulate sufficient events for reliable inferential conclusions. This paper introduces a Bayesian adaptive method for the single-arm trial design that provides efficient analysis of survival data under these constraints. By incorporating data from previous studies to establish prior knowledge and a historical control arm, this approach enables robust and accurate estimations and predictions for trial design, sample size determination, and inferential decision-making. To support the implementation of this method, we developed an R package called “BayesAT,” which offers significant flexibility in modelling and supports multi-stage interim analyses, particularly for evaluating de-intensified oncologic treatments.