Practitioner's Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls

Latent class analysis is a probabilistic modeling algorithm that allows clustering of data and statistical inference. There has been a recent upsurge in the application of latent class analysis in the fields of critical care, respiratory medicine, and beyond. In this review, we present a brief overv...

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Veröffentlicht in:Critical care medicine Jg. 49; H. 1; S. e63
Hauptverfasser: Sinha, Pratik, Calfee, Carolyn S, Delucchi, Kevin L
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.01.2021
Schlagworte:
Data Interpretation, Statistical
Humans
Latent Class Analysis
Statistics as Topic
ISSN:1530-0293, 1530-0293
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Zusammenfassung:Latent class analysis is a probabilistic modeling algorithm that allows clustering of data and statistical inference. There has been a recent upsurge in the application of latent class analysis in the fields of critical care, respiratory medicine, and beyond. In this review, we present a brief overview of the principles behind latent class analysis. Furthermore, in a stepwise manner, we outline the key processes necessary to perform latent class analysis including some of the challenges and pitfalls faced at each of these steps. The review provides a one-stop shop for investigators seeking to apply latent class analysis to their data.
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SourceType-Scholarly Journals-1
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ISSN:1530-0293
1530-0293
DOI:10.1097/CCM.0000000000004710