ArticleTrials2025
An efficient single-arm Bayesian adaptive trial algorithm to evaluate de-intensified oncologic treatment.
Article in Trials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
backgroundIn 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.
methodThis 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.
resultOur approach is validated through comprehensive simulation studies and sensitivity analyses. Additionally, this algorithm has been applied to a pediatric Hodgkin lymphoma trial, showcasing its capability to effectively leverage information from previous studies and conduct interim analyses that expedite conclusions regarding treatment efficacy.
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