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Practitioner’s Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls
Open accessAbstract
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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“LCGA facilitates the classification of individuals according to their most likely trajectory, although findings should be interpreted with caution, as larger sample sizes are generally preferable when using this technique. (Sinha et al., 2021).”
From Long-term cardiometabolic effects of early institutionalization and foster care: Evidence from the Bucharest Early Intervention Project (2026) · cites this paper as Sinha et al., 2021
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“In LCA, the probabilities of class membership are obtained, which in turn allows statistical inference when determining the most appropriate number of classes for a population [44], not clear-cut class assignments as occurs in cluster analysis.”
From Recommendations for empirical syndemics analyses: A stepwise methodological guide (2024) · cites this paper as [44]
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“In this context, although our four-class BMI trajectory model demonstrated reasonable entropy (0.88), suggesting accurate class separation, the results of the LCGA should be interpreted with caution, as a larger sample size would likely provide higher fit accuracy and greater generalizability (Sinha et al., 2021).”
From Long-term cardiometabolic effects of early institutionalization and foster care: Evidence from the Bucharest Early Intervention Project (2026) · cites this paper as Sinha et al., 2021
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Metadata
- Authors
- Sinha, P., Calfee, C. S., & Delucchi, K. L.
- Affiliations
- University of California, San Francisco (US)
- Venue
- Critical Care Medicine
- Published
- 2020-11-09
- Volume
- 49
- Issue
- 1
- Pages
- e63-e79
- ISSN
- 0090-3493 (ISSN-L)
- PMC
- PMC7746621
- OpenAlex
- W3102547229
- Cited by
- 1,204
- References
- 56
- Citations by year
- 2026: 386 · 2025: 382 · 2024: 290 · 2023: 98 · 2022: 35 · 2021: 13
- FWCI
- 7.14
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Critical Care Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Heart, Lung, and Blood Institute (R35HL140026)
- National Institute of General Medical Sciences (T32GM008440)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- latent class analysis, clustering, stochastic modeling, statistical inference, intensive care, respiratory medicine, methodological pitfalls, data analysis pipeline
- MeSH terms
- Latent Class Analysis, Data Interpretation, Statistical, Humans, Statistics as Topic
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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