This code demonstrates how to use a latent class moderator to examine heterogeneity in intervention effects among adolescents receiving treatment for cannabis use. First, the code identifies latent classes of contextual and individual risk at baseline using LCA. Then, it uses an adjusted 3-step approach with BCH weights to regress the outcomes on level of care, latent class membership, the interaction between them, and covariates.

This code fits a 4-class, latent-class model for marijuana use and attitudes using a model-based approach (1-step approach). It includes a covariate for grades in the model.

This code fits a 4-class, latent-class model for marijuana use and attitudes using a model-based approach (1-step approach). It includes a covariate for grades and a grouping variable for year in the model.

This code fits a baseline, latent-profile model for the “Big 5” personality traits using 5 continuous indicators of the latent class variable and biological sex as a covariate.

This model estimates six day-level latent classes of substance use consequences using a marginal modeling approach to account for the nested data structure. One one day-level covariate (daily positive affect) and person-level covariate (student is under legal drinking age) are included to predict consequence latent class membership.

This code fits a 2-level latent-class model with covariates using a “non-parametric approach.” This model simultaneously estimates day-level classes of substance-related consequences and person-level classes that group individuals based on proportion of each type of day. This model includes covariates at each level to estimate associations with both the day-level and person-level classes.