A Bayesian method for addressing multinomial misclassification with applications for alcohol epidemiological modeling

Abstract

In this article, we describe a new command, bamm, that implements a Bayesian method for addressing misclassification in multinomial data; see Swartz et al. (2004, Canadian Journal of Statistics 32: 285–302). We also describe a postestimation command, bammdx, that was developed to provide additional estimation diagnostics. We describe the method and the new commands and then present results from both a simulation study demonstrating bamm’s performance under a known misclassification data-generating process and an empirical example from alcohol epidemiology modeling.