Bayesian Analysis and Design for Joint Modeling of Two Binary Responses With Misclassification

Abstract

Survey data are often subject to various types of errors such as misclassification. In this article, we consider a model where interest is simultaneously in two correlated response variables and one is potentially subject to misclassification. A motivating example of a recent study of the impact of a sexual education course for adolescents is considered. A simulation-based sample size determination scheme is applied to illustrate the impact of misclassification on power and bias for the parameters of interest.

Bayesian Statistics in Sociology: Past, Present, and Future

“…They found that models that did not address the potential misclassification of children yielded smaller effects of family type on disability propensity. Stamey et al (2017) developed a Bayesian approach to address misclassification of outcome variables. They assumed there are two response variables, both binary, with one variable subject to missclassification.…”