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 incor-porates 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 in-formation, 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.
Original language | English (US) |
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Pages (from-to) | 2481-2504 |
Number of pages | 24 |
Journal | Annals of Applied Statistics |
Volume | 16 |
Issue number | 4 |
DOIs | |
State | Published - Dec 2022 |
Keywords
- Bayesian adaptive method
- meta-analysis
- phase I clinical trials
- power prior
- random-effects model
ASJC Scopus subject areas
- Statistics and Probability
- Modeling and Simulation
- Statistics, Probability and Uncertainty
MD Anderson CCSG core facilities
- Biostatistics Resource Group