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dc.contributor.authorUlbricht, Christine M.
dc.contributor.authorChrysanthopoulou, Stavroula A.
dc.contributor.authorLevin, Len
dc.contributor.authorLapane, Kate L.
dc.date2022-08-11T08:09:17.000
dc.date.accessioned2022-08-23T16:24:44Z
dc.date.available2022-08-23T16:24:44Z
dc.date.issued2018-03-17
dc.date.submitted2018-04-02
dc.identifier.citationPsychiatry Res. 2018 Mar 17. pii: S0165-1781(17)30312-8. doi: 10.1016/j.psychres.2018.03.003. [Epub ahead of print]
dc.identifier.issn1872-7123
dc.identifier.doi10.1016/j.psychres.2018.03.003
dc.identifier.pmid29605104
dc.identifier.urihttp://hdl.handle.net/20.500.14038/36194
dc.description.abstractDepression is a significant public health problem but symptom remission is difficult to predict. This may be due to substantial heterogeneity underlying the disorder. Latent class analysis (LCA) is often used to elucidate clinically relevant depression subtypes but whether or not consistent subtypes emerge is unclear. We sought to critically examine the implementation and reporting of LCA in this context by performing a systematic review to identify articles detailing the use of LCA to explore subtypes of depression among samples of adults endorsing depression symptoms. PubMed, PsycINFO, CINAHL, Scopus, and Google Scholar were searched to identify eligible articles indexed prior to January 2016. Twenty-four articles reporting 28 LCA models were eligible for inclusion. Sample characteristics varied widely. The majority of articles used depression symptoms as the observed indicators of the latent depression subtypes. Details regarding model fit and selection were often lacking. No consistent set of depression subtypes was identified across studies. Differences in how models were constructed might partially explain the conflicting results. Standards for using, interpreting, and reporting LCA models could improve our understanding of the LCA results. Incorporating dimensions of depression other than symptoms, such as functioning, may be helpful in determining depression subtypes.
dc.language.isoen_US
dc.publisherElsevier/North-Holland Biomedical Press
dc.relation<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=29605104&dopt=Abstract">Link to article in PubMed</a>
dc.relation.urlhttps://doi.org/10.1016/j.psychres.2018.03.003
dc.subjectDepressive Disorder/classification
dc.subjectDepression
dc.subjectDepression subtypes
dc.subjectFinite mixture model
dc.subjectLatent class analysis
dc.subjectLibrary and Information Science
dc.subjectPsychiatry
dc.subjectPsychiatry and Psychology
dc.titleThe use of latent class analysis for identifying subtypes of depression: A systematic review.
dc.typeJournal Article
dc.source.journaltitlePsychiatry research
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/lib_articles/207
dc.identifier.contextkey11886202
html.description.abstract<p>Depression is a significant public health problem but symptom remission is difficult to predict. This may be due to substantial heterogeneity underlying the disorder. Latent class analysis (LCA) is often used to elucidate clinically relevant depression subtypes but whether or not consistent subtypes emerge is unclear. We sought to critically examine the implementation and reporting of LCA in this context by performing a systematic review to identify articles detailing the use of LCA to explore subtypes of depression among samples of adults endorsing depression symptoms. PubMed, PsycINFO, CINAHL, Scopus, and Google Scholar were searched to identify eligible articles indexed prior to January 2016. Twenty-four articles reporting 28 LCA models were eligible for inclusion. Sample characteristics varied widely. The majority of articles used depression symptoms as the observed indicators of the latent depression subtypes. Details regarding model fit and selection were often lacking. No consistent set of depression subtypes was identified across studies. Differences in how models were constructed might partially explain the conflicting results. Standards for using, interpreting, and reporting LCA models could improve our understanding of the LCA results. Incorporating dimensions of depression other than symptoms, such as functioning, may be helpful in determining depression subtypes.</p>
dc.identifier.submissionpathlib_articles/207
dc.contributor.departmentLamar Soutter Library
dc.contributor.departmentDepartment of Quantitative Health Sciences


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