Bayesian Hierarchical Random-Effects Meta-Analysis and Design of Phase I Clinical Trials

Authors

Ruitao Lin
Haolun Shi
Guosheng Yin
Peter F Thall
Ying Yuan
Christopher R Flowers

Publication Date

12-1-2022

Journal

Annals of Applied Statistics

Abstract

We propose a curve-free random-effects meta-analysis approach to combining data from multiple phase I clinical trials to identify an optimal dose. Our method accounts for between-study heterogeneity that may stem from different study designs, patient populations, or tumor types. We also develop a meta-analytic-predictive (MAP) method based on a power prior that incorporates data from multiple historical studies into the design and conduct of a new phase I trial. Performances of the proposed methods for data analysis and trial design are evaluated by extensive simulation studies. The proposed random-effects meta-analysis method provides more reliable dose selection than comparators that rely on parametric assumptions. The MAP-based dose-finding designs are generally more efficient than those that do not borrow information, especially when the current and historical studies are similar. The proposed methodologies are illustrated by a meta-analysis of five historical phase I studies of Sorafenib, and design of a new phase I trial.

Keywords

Bayesian adaptive method, meta-analysis, phase I clinical trials, power prior, random-effects model

DOI

10.1214/22-aoas1600

PMID

36329718

PMCID

PMC9624503

PubMedCentral® Posted Date

12-1-2022

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes