Latent transition analysis for longitudinal data

Abstract

Assessing outcome is a critical problem for the study of addictive behaviors. Traditional approaches often lack power and sensitivity. Latent Transition Analysis is an alternative procedure that is applicable to categorical latent variable models such as stage models. The method involves four different types of parameters, each of which may be relevant to different research questions. Two examples that employ the Stages of Change construct are used to illustrate the method. In the first example, three different models of longitudinal change are compared. In the second example, the effects of an expert system intervention for smoking is compared to a control condition. The method permits the investigation of a series of specific comparisons: (1) the effectiveness of the intervention for individuals in different stages can be assessed; (2) the effectiveness of the intervention can be evaluated for different time intervals; and (3) the effects of intervention on both progression through the stages and regression through the stages or relapse can be assessed. Other potential applications of the method are also discussed.