title: Symptom Cluster Research With Biomarkers and Genetics Using Latent Class Analysis.
abstract: The purpose of this article is to provide an overview of latent class analysis (LCA) and examples from symptom cluster research that includes biomarkers and genetics. A review of LCA with genetics and biomarkers was conducted using Medline, Embase, PubMed, and Google Scholar. LCA is a robust latent variable model used to cluster categorical data and allows for the determination of empirically determined symptom clusters. Researchers should consider using LCA to link empirically determined symptom clusters to biomarkers and genetics to better understand the underlying etiology of symptom clusters. The full potential of LCA in symptom cluster research has not yet been realized because it has been used in limited populations, and researchers have explored limited biologic pathways.
authors.affiliation: 1 Yale University, New Haven, CT, USA.
authors.forename: Samantha
authors.initials: S
authors.lastname: Conley
mesh_terms: D015415:Biomarkers; D005823:Genetics / Q000639:trends*; D006801:Humans; D015233:Models, Statistical; D012106:Research / Q000639:trends*; D013577:Syndrome
chemical_list: D015415:Biomarkers
keywords: biomarkers; genetics; latent class analysis; symptom clusters