Bayesian Estimation of Logistic Regression with Misclassified Covariates and Response for Educational Psychology Data

• Activity Number: 424
• Type: Contributed
• Date/Time: Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Brandi Falley*+ and James Stamey and Alex Beaujean
• Companies: Baylor University and Baylor University and Baylor University
• Keywords: Bayesian Estimation ; Logistic Regression ; Misclassified Covariates ; Educational Psychology

Abstract

Measurement error problems in binary regression are of considerable interest among researchers, especially in epidemiological studies. Misclassification can be considered a special case of measurement error specifically for the situation when measurement is the categorical classification of items. Bayesian methods offer practical advantages for the analysis of epidemiological data including the possibility of incorporating relevant prior scientific information and the ability to make inferences that do not rely on large sample assumptions. In this presentation we consider a logistic regression model where both the response and a binary covariate are subject to misclassification. We assume both a continuous measure and a binary diganostic test are available for the response variable but no gold standard test is assumed available. We consider a fully Bayesian analysis that affords such adj