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

By Stephanie T. Lanza, Brittany L. Rhoades. Prevention Science, 2013

1,124 citations recorded by the source corpus.

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.

Subfields: Experimental and Cognitive Psychology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/cf9abe41369048cba109c6d66fb99841fae3345a

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Citations in the corpus

Latent Class Analysis With Distal Outcomes: A Flexible Model-Based Approach (2013, methodology): “Grouping variables can be included in the downloadable Excel calculator (mentioned earlier, available for download at methodology.psu.edu) that implements the model-based approach for a categorical distal outcome (Lanza & Rhoades, 2011b).”
Everyday Understandings of Happiness, Good Life, and Satisfaction: Three Different Facets of Well-being (2017, background): “The classes identified should therefore be considered a heuristic rather than a “true” representation of the heterogeneity across the individuals studied (Lanza and Rhoades 2013; Geiser 2013).”
A Comparison of Three-Step Approaches for Auxiliary Variables in Latent Class and Latent Profile Analysis (2017, background): “Distal outcome variable Applied examples of distal outcomes can be found in Lanza and Rhoades (2013), Lanza and Cooper (2016), Ulbricht, Rothschild, and Lapane (2015), and Jiang et al. (2016).”
Universal Interventions: Fully Exploring Their Impacts and Potential to Produce Population-Level Impacts (2017, background): “In contrast to variable-centered approaches, person-oriented techniques do not treat small deviations from normal distributions as error, but rather as potential sources of information to be evaluated (Lanza & Rhoades, 2012; Shiyko, Ram, & Grimm, 2012).”
Practitioner’s Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls. (2020, background): “In instances where class-membership carries greater uncertainty, Lanza and Rhoades (58) found this simplistic approach was less conducive to Figure 5.”