This study introduces latent class analysis (LCA) as an alternative approach to subgroup analysis in prevention and treatment research. Traditional subgroup analysis aims to determine whether individuals respond differently to a treatment based on measured characteristics, but it faces methodological challenges such as high Type I error rates, low statistical power, and limitations in examining higher-order interactions. LCA identifies underlying subgroups characterized by multiple dimensions, which can be used to examine differential treatment effects. The study uses data from the National Longitudinal Survey of Adolescent Health to identify five latent subgroups based on six contextual characteristics: Low Risk, Peer Risk, Economic Risk, Household & Peer Risk, and Multi-Contextual Risk. Two approaches are demonstrated to examine differential treatment effects: a classify-analyze approach using logistic regression and a model-based approach based on a reparameterization of the LCA with covariates model. The results show that the hypothetical intervention program was more effective for adolescents in the Peer Risk subgroup, reducing their risk of Grade 9 binge drinking by over 50%. The study highlights the potential of LCA in identifying meaningful subgroups and informing targeted prevention and treatment programs.This study introduces latent class analysis (LCA) as an alternative approach to subgroup analysis in prevention and treatment research. Traditional subgroup analysis aims to determine whether individuals respond differently to a treatment based on measured characteristics, but it faces methodological challenges such as high Type I error rates, low statistical power, and limitations in examining higher-order interactions. LCA identifies underlying subgroups characterized by multiple dimensions, which can be used to examine differential treatment effects. The study uses data from the National Longitudinal Survey of Adolescent Health to identify five latent subgroups based on six contextual characteristics: Low Risk, Peer Risk, Economic Risk, Household & Peer Risk, and Multi-Contextual Risk. Two approaches are demonstrated to examine differential treatment effects: a classify-analyze approach using logistic regression and a model-based approach based on a reparameterization of the LCA with covariates model. The results show that the hypothetical intervention program was more effective for adolescents in the Peer Risk subgroup, reducing their risk of Grade 9 binge drinking by over 50%. The study highlights the potential of LCA in identifying meaningful subgroups and informing targeted prevention and treatment programs.