Latent Profile/Class Analysis Identifying Differentiated Intervention Effects

Abstract

Background:The randomized clinical trial is generally considered the most rigorous study design for evaluating overall intervention effects. Because of patient heterogeneity, subgroup analysis is often used to identify differential intervention effects. In research of behavioral interventions, such subgroups often depend on a latent construct measured by multiple correlated observed variables.Objectives: The purpose of this article was to illustrate latent class analysis/latent profile analysis as a helpful tool to characterize latent subgroups, conduct exploratory subgroup analysis, and identify potential differential intervention effects using clinical trial data.Methods: After reviewing different approaches for subgroup analysis, latent class analysis/latent profile analysis was chosen to identify heterogeneous patient groups based on multiple correlated variables. This approach is superior in this specific scenario because of its ability to control Type I error, assess intersection of multiple moderators, and improve interpretability. We used a case study example to illustrate the process of identifying latent classes as potential moderators based on both clinical and perceived risk scores and then tested the differential effects of health coaching in improving health behavior for patients with elevated risk of developing coronary heart disease.Results: We identified three classes based on one clinical risk score and four perceived risk measures for individuals with high risk of developing coronary heart disease. Compared to other classes we assessed, individuals in the class with low clinical risk and low perceived risk benefit most from health coaching to improve their physical activity levels.Discussion: Latent class analysis/latent profile analysis offers a person-centered approach to identifying distinct patient profiles that can be used as moderators for subgroup analysis. This offers tremendous opportunity to identify differential intervention effects in behavioral research.

Symptom clusters and unplanned hospital readmission in Chinese patients with acute myocardial infarction on admission

“…(3) Likelihood ratio metrics: Lo-Mendell-Rubin (LMR) and Bootstrapped likelihood ratio test (BLRT), significant LMR, BLRT values indicate that a model with K categories outperforms a model with K-1 categories (27,28).…”

“…(2) Entropy index: indicates the degree of classification accuracy, the value range is 0–1, the closer to 1 indicates that the classification is more accurate. (3) Likelihood ratio metrics: Lo-Mendell-Rubin (LMR) and Bootstrapped likelihood ratio test (BLRT), significant LMR, BLRT values indicate that a model with K categories outperforms a model with K-1 categories ( 27 , 28 ).…”

Health literacy in patients with gout: A latent profile analysis

“…Latent profile analysis is a person-centered approach that groups individuals homogeneously based on differences in their scores on each variable (dimensions of health literacy), which is conducive to studying the characteristics of different types of populations [ 26 , 27 ]. This approach categorizes individuals into different groups to help target interventions to different subgroups, which can lead to better intervention outcomes, especially for disadvantaged patients [ 28 , 29 ]. Therefore, in this study, we surveyed patients with gout using our team-designed Health Literacy Scale, which covers the three dimensions of Nutbeam’s Health Literacy Theory and is a disease-specific scale.…”

Perceived professional preparedness and identity among senior nursing students: a latent profile Analysis

“…To address these problems, latent profile analysis (LPA) may be appropriate. LPA is a person-centred approach that can identify subgroups of participants who share similar patterns based on variables of interest [ 19 ]. By using LPA, researchers can establish potentially different patterns of perceived professional preparedness among senior nursing students.…”