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Latent Profile/Class Analysis Identifying Differentiated Intervention Effects.

Publication ,  Journal Article
Yang, Q; Zhao, A; Lee, C; Wang, X; Vorderstrasse, A; Wolever, RQ
Published in: Nurs Res
September 2022

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.

Duke Scholars

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Published In

Nurs Res

DOI

EISSN

1538-9847

Publication Date

September 2022

Volume

71

Issue

5

Start / End Page

394 / 403

Location

United States

Related Subject Headings

  • Risk Factors
  • Nursing
  • Latent Class Analysis
  • Humans
  • 4205 Nursing
  • 4204 Midwifery
  • 1110 Nursing
 

Citation

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Yang, Q., Zhao, A., Lee, C., Wang, X., Vorderstrasse, A., & Wolever, R. Q. (2022). Latent Profile/Class Analysis Identifying Differentiated Intervention Effects. Nurs Res, 71(5), 394–403. https://doi.org/10.1097/NNR.0000000000000597
Yang, Qing, Amy Zhao, Chiyoung Lee, Xiaofei Wang, Allison Vorderstrasse, and Ruth Q. Wolever. “Latent Profile/Class Analysis Identifying Differentiated Intervention Effects.Nurs Res 71, no. 5 (September 2022): 394–403. https://doi.org/10.1097/NNR.0000000000000597.
Yang Q, Zhao A, Lee C, Wang X, Vorderstrasse A, Wolever RQ. Latent Profile/Class Analysis Identifying Differentiated Intervention Effects. Nurs Res. 2022 Sep;71(5):394–403.
Yang, Qing, et al. “Latent Profile/Class Analysis Identifying Differentiated Intervention Effects.Nurs Res, vol. 71, no. 5, Sept. 2022, pp. 394–403. Pubmed, doi:10.1097/NNR.0000000000000597.
Yang Q, Zhao A, Lee C, Wang X, Vorderstrasse A, Wolever RQ. Latent Profile/Class Analysis Identifying Differentiated Intervention Effects. Nurs Res. 2022 Sep;71(5):394–403.

Published In

Nurs Res

DOI

EISSN

1538-9847

Publication Date

September 2022

Volume

71

Issue

5

Start / End Page

394 / 403

Location

United States

Related Subject Headings

  • Risk Factors
  • Nursing
  • Latent Class Analysis
  • Humans
  • 4205 Nursing
  • 4204 Midwifery
  • 1110 Nursing