Bayesian regression models adjusting for unidirectional covariate misclassification

In this article we consider unidirectional covariate misclassification, meaning that the direction of classification error is known. We investigate the identifiability of Bayesian regression models when a binary covariate is subject to unidirectional misclassification. In the Bayesian framework we consider whether knowledge of the direction of error suffices, so that adjustment for misclassification can be undertaken without any source of information on the magnitude of error. Although measurement error models are generally non-identified without such information, for the case of unidirectional misclassification, we do obtain model identifiability when the response variable is non-binary. For the binary response model that is non-identified we examine the extent of partial identification. The limiting posterior distributions of the parameters are obtained for this partially identified model, for two different prior distributions. We perform computational studies that illustrate statistical learning, for the three cases where the model is easily identified, weakly identified, and partially identified. A case study is performed using real data. (C) 2016 Statistical Society of Canada