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Activity Number: 453
Type: Contributed
Date/Time: Wednesday, August 6, 2008 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract - #302346
Title: Bayesian Inference on Relative Risk Incorporating Multiple Covariates
Author(s): Qianqiu Li and Yonggang Zhao*+
Companies: Wyeth Research and Wyeth Research
Address: , , ,
Keywords: Relative risk ; Bayesian inference ; Poisson regression ; MCMC
Abstract:

Relative risk, defined as ratio of two incidence rates, is a common quantity for comparing the rates in two populations. In the presence of confounding factors, Poisson regression is usually employed to adjust relative risk. This paper implements a Bayesian Gamma-Poisson regression model to infer adjusted relative risk. Computations are carried out using Markov Chain Monte Carlo (MCMC) methods. Comparisons are made with the frequentist approaches: lognormal-Poisson regression, Log-Binomial regression, negative-Binomial regression, as well as stratified relative risk.

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