The clinical course of alcohol use disorders: Using joinpoint analysis to aid in interpretation of growth mixture models

Background: The clinical course of alcohol use disorders (AUD) is marked by great heterogeneity both within and between individuals. One approach to modeling this heterogeneity is latent growth mixture modeling (LGMM), which identifies a number of latent subgroups of drinkers with drinking trajectories that are similar within a latent subgroup but different between subgroups. LGMM is data-driven and uses an iterative process of testing a sequential number researcher-selected of latent subgroups then selecting the best fitting model. Despite the advantages of LGMM (e.g., identifying subgroups among heterogeneous longitudinal data), one limitation is the lack of precision of LGMM to model abrupt changes in drinking during treatment that are often observed by clinicians. Joinpoint analysis (JPA) is a data analysis procedure that is used to identify discrete change points in longitudinal data (e.g., changes from increasing to decreasing or decreasing to increasing). Method: This study presents a demonstration of using JPA as a post hoc procedure for LGMM to improve accuracy in modeling abrupt changes in clinical course of AUD. Results: Results from this secondary data analysis of 549 AUD participants participating in the NIAAA sponsored relapse replication and extension project uncovered four latent classes of drinking trajectories. Discussion: Within these trajectories the addition of JPA improved precision in modeling the clinical course of AUDs.