Bayesian censored piecewise regression mixture models with skewness

Abstract

ABSTRACT This paper presents censored mixture regression models with piecewise growth curves for assessing longitudinal data that exhibit multiphasic features. Such features may include censoring, skewness, measurement errors in covariates, and mixtures of unobserved subpopulations. In the process of describing those features, identification of differential effects of predictors on a response variable for a heterogeneous population (subpopulations) has recently been highly sought. Regression mixture models are key methods for assessing differential effects of predictors. In this article, we extend regression mixture models with normal distribution to incorporate (i) skew-normal distribution, (ii) left-censoring, (iii) measurement errors, and (iv) piecewise growth mixture modeling for describing multiphasic trajectories over time where the observed observations come from a mixture of unobserved subgroups. The proposed methods are illustrated using real data from an AIDS clinical study and a Bayesian approach.