A Bayesian Dynamic Model-Based Adaptive Design for Oncology Dose Optimization in Phase I/II Clinical Trials.

ABSTRACT: With the development of targeted therapy, immunotherapy, and antibody-drug conjugates (ADCs), there is growing concern over the "more is better" paradigm developed decades ago for chemotherapy, prompting the US Food and Drug Administration (FDA) to initiate Project Optimus to reform dose optimization and selection in oncology drug development. For early-phase oncology trials, given the high variability from sparse data and the rigidity of parametric model specifications, we use Bayesian dynamic models to borrow information across doses with only vague order constraints. Our proposed adaptive design simultaneously incorporates toxicity and efficacy outcomes to select the optimal dose (OD) in Phase I/II clinical trials, utilizing Bayesian model averaging to address the uncertainty of dose-re

SUBMITTER: Qiu Y 

PROVIDER: S-EPMC11889989 | biostudies-literature | 2025 Mar-Apr

REPOSITORIES: biostudies-literature