Latent Class Analysis: An Alternative Perspective on Subgroup Analysis in Prevention and Treatment

The overall goal of this study is to introduce latent class analysis (LCA) as an alternative approach to latent subgroup analysis.
Traditionally, subgroup analysis aims to determine whether individuals respond differently to a treatment based on one or
more measured characteristics. LCA provides a way to identify a small set of underlying subgroups characterized by multiple
dimensions which could, in turn, be used to examine differential treatment effects. This approach can help to address methodological
challenges that arise in subgroup analysis, including a high Type I error rate, low statistical power, and limitations in
examining higher-order interactions. An empirical example draws on N = 1,900 adolescents from the National Longitudinal Survey of Adolescent Health. Six characteristics (household poverty, single-parent
status, peer cigarette use, peer alcohol use, neighborhood unemployment, and neighborhood poverty) are used to identify five
latent subgroups: Low Risk, Peer Risk, Economic Risk, Household & Peer Risk, and Multi-Contextual Risk. Two approaches for
examining differential treatment effects are demonstrated using a simulated outcome: 1) a classify-analyze approach and, 2)
a model-based approach based on a reparameterization of the LCA with covariates model. Such approaches can facilitate targeting
future intervention resources to subgroups that promise to show the maximum treatment response.