2016
DOI: 10.1177/0193945916679812
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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 b… Show more

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Cited by 23 publications
(24 citation statements)
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“…Person-oriented approaches, such as latent class and cluster analyses, are used to identify subgroups of people who exhibit similar patterns of characteristics. Common person-oriented clustering methods include latent class and cluster analysis (Bergman & Trost, 2006;Collins & Lanza, 2010;Conley, 2016).…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Person-oriented approaches, such as latent class and cluster analyses, are used to identify subgroups of people who exhibit similar patterns of characteristics. Common person-oriented clustering methods include latent class and cluster analysis (Bergman & Trost, 2006;Collins & Lanza, 2010;Conley, 2016).…”
Section: Discussionmentioning
confidence: 99%
“…Person‐oriented approaches, such as latent class and cluster analyses, are used to identify subgroups of people who exhibit similar patterns of characteristics. Common person‐oriented clustering methods include latent class and cluster analysis (Bergman & Trost, ; Collins & Lanza, ; Conley, ). The person‐oriented approach is particularly useful in uncovering subgroups in heterogeneous populations because the structure of the variables does not have to remain constant across the population (Magnusson, ).…”
Section: Methodsmentioning
confidence: 99%
“…We used LCA to identify latent classes of persons with similar IBS profiles based on symptom‐related variables (i.e., groups of daily diary symptoms identified by EFA, cognitive beliefs about IBS and IBS‐QOL). Individuals were classified according to their most likely latent class membership (Conley, ; Muthén & Muthén, ). Statistical fit indices were used to determine the number of latent classes in the final model and to evaluate model fit.…”
Section: Methodsmentioning
confidence: 99%
“…Statistical methods such as factor analysis can be used to examine the way in which symptoms are related to one another. An alternative approach to this variable‐centered approach is to evaluate subgroups of individuals with similar symptom patterns—a person‐centered approach (Conley, ). Using latent class analysis (LCA), for example, we can identify subgroups (i.e., latent classes) of individual patients sharing similar patterns of symptom characteristics (Conley, ).…”
Section: Introductionmentioning
confidence: 99%
“…In the era of personalized medicine, identification of molecular markers that can predict patients at risk of developing these debilitating complaints in the form of symptom cluster is important. Recent evidences support the fact that fatigue, depression, pain and sleep disturbances share some common pathways that make them a symptom cluster [7,125,126].…”
Section: Genetic Polymorphisms and Symptom Clustermentioning
confidence: 99%