Bayesian Adaptive Model Selection Design for Optimal Biological Dose Finding in Phase I/II Clinical Trials

Authors

Ruitao Lin
Guosheng Yin
Haolun Shi

Publication Date

4-14-2023

Journal

Biostatistics

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 right dose. Not only does our design possess a completely new yet flexible dose-finding framework, but it also has satisfactory and robust performance as demonstrated by extensive simulation studies. In addition, we show that our design enjoys desirable coherence properties, while most of existing phase I/II designs do not. We further extend the design to accommodate late-onset outcomes which are common in immunotherapy. The proposed design is exemplified with a phase I/II clinical trial in chronic lymphocytic leukemia.

Keywords

Humans, Bayes Theorem, Dose-Response Relationship, Drug, Maximum Tolerated Dose, Antineoplastic Agents, Computer Simulation, Research Design, Bayesian adaptive design, Bayesian model selection, Delayed response, Optimal biological dose, Phase I/II trial

DOI

10.1093/biostatistics/kxab028

PMID

34296266

PMCID

PMC10102885

PubMedCentral® Posted Date

7-23-2021

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes