title: Binomial regression with a misclassified covariate and outcome.
abstract: Misclassification occurring in either outcome variables or categorical covariates or both is a common issue in medical science. It leads to biased results and distorted disease-exposure relationships. Moreover, it is often of clinical interest to obtain the estimates of sensitivity and specificity of some diagnostic methods even when neither gold standard nor prior knowledge about the parameters exists. We present a novel Bayesian approach in binomial regression when both the outcome variable and one binary covariate are subject to misclassification. Extensive simulation results under various scenarios and a real clinical example are given to illustrate the proposed approach. This approach is motivated and applied to a dataset from the Baylor Alzheimer's Disease and Memory Disorders Center.
authors.affiliation: Division of Biostatistics, The University of Texas Health Science Center at Houston, Houston, USA sheng.t.luo@uth.tmc.edu.
authors.forename: Sheng
authors.initials: S
authors.lastname: Luo
authors.affiliation: Division of Biostatistics, The University of Texas Health Science Center at Houston, Houston, USA.
authors.forename: Wenyaw
authors.initials: W
authors.lastname: Chan
authors.affiliation: Department of Biostatistics and Medical Informatics, The University of Wisconsin-Madison, Madison, USA.
authors.forename: Michelle A
authors.initials: MA
authors.lastname: Detry
authors.affiliation: Department of Psychology, University of Houston, Houston, USA.
authors.forename: Paul J
authors.initials: PJ
authors.lastname: Massman
authors.affiliation: Department of Neurology, Baylor College of Medicine, Houston, USA.
authors.forename: Rachelle S
authors.initials: RS
authors.lastname: Doody
mesh_terms: D000544:Alzheimer Disease / Q000523:psychology; D001499:Bayes Theorem; D015982:Bias; D016010:Binomial Distribution; D056808:Biostatistics; D003198:Computer Simulation; D018450:Disease Progression; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D012044:Regression Analysis