BAYESIAN HIERARCHICAL RANDOM-EFFECTS META-ANALYSIS AND DESIGN OF PHASE I CLINICAL TRIALS

• 成果类型: Article
• 署名作者: Lin, Ruitao; Shi, Haolun; Yin, Guosheng; Thali, Peter F.; Yuan, Ying; Flowers, Christopher R.
• 署名单位: University of Texas System; UTMD Anderson Cancer Center; Simon Fraser University; University of Hong Kong; University of Texas System; UTMD Anderson Cancer Center
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1600
• 发表日期: 2022
• 页码: 2481-2504
• 关键词: continual reassessment method; factor receptor inhibitor; model-based metaanalysis; days on/7 days; raf kinase; historical information; prior distributions; japanese patients; drug development; power prior

摘要

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 dosefinding 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.