Bayesian hierarchical random-effects meta-analysis and design of phase I clinical trials
Ruitao Lin, Haolun Shi, Guosheng Yin, Peter F. Thall, Ying Yuan, Christopher R. Flowers
The Annals of Applied Statistics · 2022 · Volume 16 · Issue 4
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
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