A Bayesian Latent-Subgroup Platform Design for Dose Optimization

Authors

Rongji Mu
Xiaojiang Zhan
Rui Sammi Tang
Ying Yuan

Publication Date

7-1-2024

Journal

Biometrics

DOI

10.1093/biomtc/ujae093

PMID

39253988

PMCID

PMC11385043

PubMedCentral® Posted Date

9-10-2024

PubMedCentral® Full Text Version

Post-print

Abstract

The US Food and Drug Administration launched Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development, calling for the paradigm shift from finding the maximum tolerated dose to the identification of optimal biological dose (OBD). Motivated by a real-world drug development program, we propose a master-protocol-based platform trial design to simultaneously identify OBDs of a new drug, combined with standards of care or other novel agents, in multiple indications. We propose a Bayesian latent subgroup model to accommodate the treatment heterogeneity across indications, and employ Bayesian hierarchical models to borrow information within subgroups. At each interim analysis, we update the subgroup membership and dose-toxicity and -efficacy estimates, as well as the estimate of the utility for risk-benefit tradeoff, based on the observed data across treatment arms to inform the arm-specific decision of dose escalation and de-escalation and identify the OBD for each arm of a combination partner and an indication. The simulation study shows that the proposed design has desirable operating characteristics, providing a highly flexible and efficient way for dose optimization. The design has great potential to shorten the drug development timeline, save costs by reducing overlapping infrastructure, and speed up regulatory approval.

Keywords

Bayes Theorem, Humans, Maximum Tolerated Dose, Computer Simulation, Dose-Response Relationship, Drug, Antineoplastic Agents, Drug Development, Models, Statistical, United States, United States Food and Drug Administration, Neoplasms, Research Design, Biometry, Adaptive Clinical Trials as Topic, Bayesian adaptive design, latent subgroup, optimal biological dose, platform trial

Published Open-Access

yes

Recommended Citation

Mu, Rongji; Zhan, Xiaojiang; Tang, Rui Sammi; et al., "A Bayesian Latent-Subgroup Platform Design for Dose Optimization" (2024). Faculty, Staff and Student Publications. 5184.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/5184