JSM 2004 - Toronto

Abstract #300401

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Activity Number: 381
Type: Contributed
Date/Time: Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #300401
Title: Non-informative Priors for the Interval Estimation of Attributable Risk
Author(s): Bin Huang*+ and Siva Sivaganesan and Gail Slap
Companies: Children's Hospital Medical Center and University of Cincinnati and Cincinnati Children's Hospital Medical Center
Address: 3333 Burnet Ave., Cincinnati, OH, 45229-3039,
Keywords: attributable risk ; Jeffery's prior ; entropy prior ; interval estimation ; simulation study
Abstract:

Attributable risk (AR) is one the most important epidemiological measure of the impact of a risk factor on an outcome. While there is a vast amount of literature on the interval estimation of the AR, very little has been done in the Bayesian approach to the interval estimate of AR. Our earlier study suggested a need for "better" noninformative priors for Bayesian interval estimation of AR. We derived an entropy prior, and assessed its performance and compared it with Jeffery's prior and other existing approaches. The study suggested that for the interval estimation of AR in general problem settings, performance of the entropy prior with regard to the frequentist coverage rate and length is better than that of Jeffery's, while these two priors are the same in the simple 2 by 2 contingency table setting.

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