Count Regression Models with a Misclassified Covariate: A Bayesian Approach

• Activity Number: 618
• Type: Contributed
• Date/Time: Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): MaryAnn Morgan-Cox*+ and James Stamey and John W. Seaman Jr.
• Companies: Eli Lilly and Company and Baylor University and Baylor University
• Keywords: Poisson Regression ; Misclassification ; Bayesian methods ; Simulation ; Equivalent sample size

Abstract

Mismeasurment, and specifically misclassification, are inevitable in a variety of regression applications. Fallible measurement methods are often used when infallible methods are either expensive or not available. Ignoring mismeasurement will result in biased estimates for the associated regression parameters. The models presented in this discussion are designed to correct this bias and yield variance estimates reflecting the uncertainty that is introduced by flawed measurements. We consider a generalized linear model for a Poisson response. This model accounts for the misclassification associated with the binary exposure covariate. In the first portion of the analysis, diffuse priors are utilized for the regression coefficients and the effective prior sample size technique is implemented to construct informative priors for the misclassification parameters. In the second portion of the analysis we place informative priors on the regression parameters and diffuse priors on the misclassification parameters. We also present results of a simulation study that incorporates prior information for both the regression coefficients and the misclassification parameters.