首页 | 本学科首页   官方微博 | 高级检索  
     


On efficient posterior inference in normalized power prior Bayesian analysis
Authors:Zifei Han  Qiang Zhang  Min Wang  Keying Ye  Ming-Hui Chen
Affiliation:1. School of Statistics, University of International Business and Economics, Beijing, China;2. Department of Management Science and Statistics, The University of Texas at San Antonio, San Antonio, Texas, USA;3. Department of Statistics, University of Connecticut, Storrs, Connecticut, USA
Abstract:The power prior has been widely used to discount the amount of information borrowed from historical data in the design and analysis of clinical trials. It is realized by raising the likelihood function of the historical data to a power parameter δ [ 0 , 1 ] $delta in [0, 1]$ , which quantifies the heterogeneity between the historical and the new study. In a fully Bayesian approach, a natural extension is to assign a hyperprior to δ such that the posterior of δ can reflect the degree of similarity between the historical and current data. To comply with the likelihood principle, an extra normalizing factor needs to be calculated and such prior is known as the normalized power prior. However, the normalizing factor involves an integral of a prior multiplied by a fractional likelihood and needs to be computed repeatedly over different δ during the posterior sampling. This makes its use prohibitive in practice for most elaborate models. This work provides an efficient framework to implement the normalized power prior in clinical studies. It bypasses the aforementioned efforts by sampling from the power prior with δ = 0 $delta = 0$ and δ = 1 $delta = 1$ only. Such a posterior sampling procedure can facilitate the use of a random δ with adaptive borrowing capability in general models. The numerical efficiency of the proposed method is illustrated via extensive simulation studies, a toxicological study, and an oncology study.
Keywords:clinical trials  discounting  historical borrowing  importance sampling  power priors
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号