Bayesian adaptive model selection design for optimal biological dose finding in phase I/II clinical trials.

ABSTRACT: Identification of the optimal dose presents a major challenge in drug development with molecularly targeted agents, immunotherapy, as well as chimeric antigen receptor T-cell treatments. By casting dose finding as a Bayesian model selection problem, we propose an adaptive design by simultaneously incorporating the toxicity and efficacy outcomes to select the optimal biological dose (OBD) in phase I/II clinical trials. Without imposing any parametric assumption or shape constraint on the underlying dose-response curves, we specify curve-free models for both the toxicity and efficacy endpoints to determine the OBD. By integrating the observed data across all dose levels, the proposed design is coherent in dose assignment and thus greatly enhances efficiency and accuracy in pinning down the r

SUBMITTER: Lin R 

PROVIDER: S-EPMC10102885 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature