Posterior Predictive Design for Phase I Clinical Trials

Authors

Chenqi Fu
Shouhao Zhou
J Jack Lee

Language

English

Publication Date

6-3-2025

Journal

Journal of the American Statistical Association

DOI

10.1080/01621459.2025.2484044

PMID

41394948

PMCID

PMC12700609

PubMedCentral® Posted Date

12-13-2025

PubMedCentral® Full Text Version

Author MSS

Abstract

Interval-based designs represent cutting-edge adaptive methodologies for phase I clinical trials to identify the maximum tolerated dose (MTD). These designs exhibit robust performance comparable to more intricate, model-based designs, and their pretabulated decision rule enables them to be implemented as simply as the conventional algorithm-based designs. In this paper, we introduce the posterior predictive (PoP) design, a novel interval-based design that leverages advanced Bayesian predictive hypothesis testing techniques for dose escalation and de-escalation. Our work moves beyond the existing model-assisted interval-based designs by achieving global optimality in dose transition. Theoretically, the global optimality ensures that the proposed design can consistently select the true MTD at an impressive convergence rate of 𝑛−1/2. Through extensive simulation studies, we demonstrate that the PoP design yields substantial improvement in operating characteristics to identify MTD, thereby presenting a valuable upgrade to the popular interval-based designs in practice. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Keywords

BOIN design, Interval-based designs, Model-assisted design, Predictive Bayes factor, PoP design

Published Open-Access

yes

Recommended Citation

Chenqi Fu, Shouhao Zhou, and J Jack Lee, "Posterior Predictive Design for Phase I Clinical Trials" (2025). Faculty, Staff and Student Publications. 6066.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6066