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 one or more measured characteristics. LCA identifies a small set of underlying subgroups characterized by multiple dimensions, which can be used to examine differential treatment effects. This approach addresses methodological challenges such as high Type I error rates, low statistical power, and limitations in examining higher-order interactions. An empirical example using data from 1,900 adolescents identified five latent subgroups: Low Risk, Peer Risk, Economic Risk, Household & Peer Risk, and Multi-Contextual Risk. Two approaches were used to examine differential treatment effects: a classify-analyze approach and a model-based approach. The results showed that the intervention was more effective for adolescents in the Peer Risk subgroup, reducing Grade 9 binge drinking. The study highlights the importance of using LCA to identify subgroups that may respond differently to treatment, allowing for more targeted interventions. LCA provides a statistically sophisticated framework for identifying subgroups of individuals that are not directly observable, and can be used to address many of the statistical challenges posed by traditional subgroup analysis. The study also discusses the limitations of using LCA, including the need for careful consideration of recoding continuous indicators and the importance of model validation. Overall, the study demonstrates the promise of a latent subgroups perspective in advancing subgroup analyses to better inform prevention and treatment sciences.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 one or more measured characteristics. LCA identifies a small set of underlying subgroups characterized by multiple dimensions, which can be used to examine differential treatment effects. This approach addresses methodological challenges such as high Type I error rates, low statistical power, and limitations in examining higher-order interactions. An empirical example using data from 1,900 adolescents identified five latent subgroups: Low Risk, Peer Risk, Economic Risk, Household & Peer Risk, and Multi-Contextual Risk. Two approaches were used to examine differential treatment effects: a classify-analyze approach and a model-based approach. The results showed that the intervention was more effective for adolescents in the Peer Risk subgroup, reducing Grade 9 binge drinking. The study highlights the importance of using LCA to identify subgroups that may respond differently to treatment, allowing for more targeted interventions. LCA provides a statistically sophisticated framework for identifying subgroups of individuals that are not directly observable, and can be used to address many of the statistical challenges posed by traditional subgroup analysis. The study also discusses the limitations of using LCA, including the need for careful consideration of recoding continuous indicators and the importance of model validation. Overall, the study demonstrates the promise of a latent subgroups perspective in advancing subgroup analyses to better inform prevention and treatment sciences.