Binary regression with misclassified response and covariate subject to measurement error: a bayesian approach.
We consider a Bayesian analysis for modeling a binary response that is subject to misclassification. Additionally, an explanatory variable is assumed to be unobservable, but measurements are available on its surrogate. A binary regression model is developed to incorporate the measurement error in the covariate as well as the misclassification in the response. Unlike existing methods, no model parameters need be assumed known. Markov chain Monte Carlo methods are utilized to perform the necessary computations. The methods developed are illustrated using atomic bomb survival data. A simulation experiment explores advantages of the approach.
McGlothlin
Anna
A
Exploratory Program Medical Statistics, Eli Lilly and Company, Lilly Corporate Center DC 0710, Indianapolis, IN 46285, USA. mcglothlinan@lilly.com
Stamey
James D
JD
Seaman
John W
JW
D001499:Bayes Theorem; D015982:Bias; D015331:Cohort Studies; D003198:Computer Simulation; D006801:Humans; D008390:Markov Chains; D009010:Monte Carlo Method; D009381:Neoplasms, Radiation-Induced / Q000401:mortality; D054043:Nuclear Weapons; D012044:Regression Analysis