
Faculty, Staff and Student Publications
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
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Medical Genetics Commons, Oncology Commons