Bayesian Quantile Regression for Misclassified Binary Data

Thomas M. Menino Convention & Exhibition Center 

Quantile regression provides a flexible framework for characterizing covariate effects across the entire conditional distribution of outcomes, rather than focusing solely on the mean. In studies with binary outcomes, the observed response is often subject to misclassification, which can introduce substantial bias. To address this challenge, we propose a Bayesian quantile regression framework that explicitly accounts for misclassification in binary outcomes by introducing a latent true outcome and incorporating sensitivity and specificity parameters. Extensive simulation studies are conducted under varying degrees of misclassification, prior specifications, and effective sample sizes. The proposed approach is illustrated through an application examining the effect of female employment status on the likelihood of domestic violence.

Quantile regression

Misclassification

Markov chain Monte Carlo

Bayesian methods 

Section on Bayesian Statistical Science