alexa Bayesian Analysis Using Power Priors with Application t
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

Bayesian Analysis Using Power Priors with Application to Pediatric Quality of Care

Brian Neelon1* and A. James O’Malley2

1Nicholas School of the Environment, Duke University, Durham, North Carolina, USA

2Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA

*Corresponding Author:
Brian Neelon
Children’s Environmental Health Initiative
Nicholas School of the Environment
Box 90328 Duke University
Durham, NC 27708-0328, USA
E-mail: [email protected]

Received Date:July 30, 2010; Accepted Date: September 28, 2010; Published Date: October 19, 2010

Citation: Neelon B, O’Malley AJ (2010) Bayesian Analysis Using Power Priors with Application to Pediatric Quality of Care. J Biomet Biostat 1:103. doi:10.4172/2155-6180.1000103

Copyright: © 2010 Neelon B, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

We illustrate how power prior distributions can be used to incorporate historical data into a Bayesian analysis when there is uncertainty about the similarity between the current and historical studies. We compare common specifications of the power prior and explore whether it is preferable to condition on the power parameter, a0 or to treat it as a random variable with a prior distribution of its own. We show that there are two natural ways of formulating the power prior for random a0. The first approach excludes the historical data in all but extreme cases and may therefore be of limited practical use. The second approach, called the normalized power prior (NPP), provides a measure of congruence between the current and historical data, so that the historical data are downweighted more substantially as the studies diverge. While this is an intuitively appealing property, our experience suggests that in real world problems involving large datasets and models with several parameters, the NPP may lead to considerably more downweighting than desired. We therefore advise practitioners to consider whether such attenuation is desirable, or whether it is more appropriate to assign a0 a fixed value based on expert opinion about the relevance of the historical data to the current analysis. We also extend the power prior to hierarchical regression models that allow covariate effects to differ across studies. We apply these methods to a pair of studies designed to improve delivery of care in pediatric clinics.

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