Binomial Regression with a Misclassified Covariate and Outcome

Authors

Sheng Luo
Wenyaw Chan
Michelle A Detry
Paul J Massman
R S. Doody

Publication Date

2-2016

Journal

Statistical Methods in Medical Research

DOI

10.1177/0962280212441965

PMID

22421539

PMCID

PMC3883897

PubMedCentral® Posted Date

1-7-2014

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes

Keywords

Alzheimer Disease, Bayes Theorem, Bias (Epidemiology), Binomial Distribution, Biostatistics, Computer Simulation, Disease Progression, Humans, Likelihood Functions, Models, Statistical, Regression Analysis

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.