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.
Funding Statement
Lin’s research was partially supported by grants from the National Cancer Institute (5P30CA016672 and 1R01CA261978).
Yin’s research was partially supported by funding from the Research Grants Council of Hong Kong (17308420).
Thall’s research was partially supported by grants from the National Cancer Institute (5P30CA016672 and 1R01CA261978).
Flowers’s research was partially supported by grants from the Cancer Prevention & Research Institute of Texas (RR190079) and Burroughs Wellcome Fund (1016433.01).
Acknowledgments
We would like to thank the Editor, the Associate Editor, and the reviewers for their valuable comments and suggestions with special thanks to the Associate Editor whose dedicated and meticulous effort has led to a much improved version of our paper.
RL and HS contributed equally to this work.
Citation
Ruitao Lin. Haolun Shi. Guosheng Yin. Peter F. Thall. Ying Yuan. Christopher R. Flowers. "Bayesian hierarchical random-effects meta-analysis and design of phase I clinical trials." Ann. Appl. Stat. 16 (4) 2481 - 2504, December 2022. https://doi.org/10.1214/22-AOAS1600
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