|Title||The power prior: theory and applications.|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Ibrahim, Joseph G., Ming-Hui Chen, Yeongjin Gwon, and Fang Chen|
|Date Published||2015 Dec 10|
|Keywords||Bayes 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 Journal||Stat Med|
|Original Publication||The power prior: Theory and applications.|
|PubMed Central ID||PMC4626399|
|Grant List||P01 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
The power prior: theory and applications.