Improving Maximum Tolerated Dose Selection in Model-Assisted Designs for Phase I Trials Through Bayesian Dose-Response Model.

ABSTRACT: Model-assisted designs have garnered significant attention in recent years due to their high accuracy in identifying the maximum tolerated dose (MTD) and their operational simplicity. To identify the MTD, they employ estimated dose limiting toxicity (DLT) probabilities via isotonic regression with pool-adjacent violators algorithm (PAVA) after trials have been completed. PAVA adjusts independently estimated DLT probabilities with the Bayesian binomial model at each dose level using posterior variances ensure the monotonicity that toxicity increases with dose. However, in small sample settings such as Phase I oncology trials, this approach can lead to unstable DLT probability estimates and reduce MTD selection accuracy. To address this problem, we propose a novel MTD identification strategy

SUBMITTER: Wakayama R 

PROVIDER: S-EPMC12553356 | biostudies-literature | 2025 Nov-Dec

REPOSITORIES: biostudies-literature