Abstract #300070

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JSM 2003 Abstract #300070
Activity Number: 128
Type: Contributed
Date/Time: Monday, August 4, 2003 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #300070
Title: Confidence Interval Estimates for Attributable Risk
Author(s): Bin Huang*+ and Siva Sivaganesan and Elizabeth Goodman and Gail Slap
Companies: Cincinnati Children's Hospital Medical Center and University of Cincinnati and Children's Hospital and Children's Hospital
Address: 336 Thrall St., Cincinnati, OH, 45220-1614,
Keywords: Attributable risk ; Asymptotic ; Bayesian ; Confidence Interval
Abstract:

Attributable risk (AR) is one of the most widely used epidemiological measurements of the importance of a risk factor in public health issues. In a recent simulation study of asymptotic confidence interval estimates, the author noted that the estimates still need improvement. These asymptotic estimates are applicable only to the cases of binary exposure. Although the concept of AR has been extended to include multilevel exposures and adjustment for other risk factors, not much work has been done on the confidence interval estimates in these general settings. The current study uses Bayesian approach to estimate AR. A general Bayesian algorithm is developed, and a simulation study is used to compare its performance with asymptotic estimates. The results suggest that Bayesian AR confidence interval estimate achieves better coverage rate and efficiency than asymptotic estimates.

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