A SAS Procedure Based on Mixture Models for Estimating Developmental Trajectories

Bobby L. Jones, Daniel S. Nagin and Kathryn Roeder


This paper introduces a new SAS procedure written by the authors that is available to analyze repeated measures data (developmental trajectories) by fitting a latent class mixture model. The TRAJ procedure fits semiparametric (discrete) mixtures of censored normal, Poisson, zero-inflated Poisson, and Bernoulli distributions to longitudinal data. Applications to psychometric scale data, offense counts and a dichotomous prevalence measure in violence research are illustrated. In addition, the use of the Bayesian Information Criterion (BIC) to address the problem of model selection, including the estimation of the number of components in the mixture, is demonstrated.

Keywords: SAS procedure TRAJ, Latent class analysis, Mixture models

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