The power prior: theory and applications.

TitleThe power prior: theory and applications.
Publication TypeJournal Article
Year of Publication2015
AuthorsIbrahim, Joseph G., Ming-Hui Chen, Yeongjin Gwon, and Fang Chen
JournalStat Med
Date Published2015 Dec 10
KeywordsBayes Theorem, Clinical Trials as Topic, Historically Controlled Study, Linear Models, Models, Statistical, Research Design, Statistics as Topic

The power prior has been widely used in many applications covering a large number of disciplines. The power prior is intended to be an informative prior constructed from historical data. It has been used in clinical trials, genetics, health care, psychology, environmental health, engineering, economics, and business. It has also been applied for a wide variety of models and settings, both in the experimental design and analysis contexts. In this review article, we give an A-to-Z exposition of the power prior and its applications to date. We review its theoretical properties, variations in its formulation, statistical contexts for which it has been used, applications, and its advantages over other informative priors. We review models for which it has been used, including generalized linear models, survival models, and random effects models. Statistical areas where the power prior has been used include model selection, experimental design, hierarchical modeling, and conjugate priors. Frequentist properties of power priors in posterior inference are established, and a simulation study is conducted to further examine the empirical performance of the posterior estimates with power priors. Real data analyses are given illustrating the power prior as well as the use of the power prior in the Bayesian design of clinical trials.

Alternate JournalStat Med
Original PublicationThe power prior: Theory and applications.
PubMed ID26346180
PubMed Central IDPMC4626399
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 GM070335 / GM / NIGMS NIH HHS / United States
GM70335 / GM / NIGMS NIH HHS / United States
P01CA142538 / CA / NCI NIH HHS / United States