Evidence map›Paper›PMID 29605104›Full record

SynthesisPsychiatry research2018

The use of latent class analysis for identifying subtypes of depression: A systematic review.

Christine M Ulbricht, Stavroula A Chrysanthopoulou, Len Levin, Kate L Lapane

Abstract readSystematic Review
In one paragraph

Synthesis in Psychiatry research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
67citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

67 citing papers in PubMed, 1 synthesis or guideline pooled it.

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7 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Christine M UlbrichtDepartment of Quantitative Health Sciences, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA 01605, USA. Electronic address: Christine.ulbricht@umassmed.edu.
Stavroula A ChrysanthopoulouDepartment of Quantitative Health Sciences, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA 01605, USA.
Len LevinLamar Soutter Library, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA.
Kate L LapaneDepartment of Quantitative Health Sciences, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA 01605, USA.

Funding

Pain Management for Older Adults Living in Nursing HomesR56NR015498 · NINR · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LAPANE, KATE L · 2016 to 2016
$297k
NINR NIH HHS R56 NR015498
6 · The paper itself

Abstract

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.

Indexed as

Latent Class AnalysisAdultDepressionFemaleHumansMaleSymptom AssessmentDepressionDepression subtypesFinite mixture modelLatent class analysis

Identifiers

PMID29605104
PMCPMC6345275

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.