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.
This research output has an Altmetric Attention Score of 19. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 March 2026.
All research outputs
#2,434,634
of 33,834,589 outputs
Outputs from Prevention Science
#167
of 1,313 outputs
Outputs of similar age
#12,812
of 258,118 outputs
Outputs of similar age from Prevention Science
#3
of 17 outputs
Altmetric has tracked 33,834,589 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,313 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.4. This one has done well, scoring higher than 87% of its peers.
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We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.
