The DOI matches its Crossref record; 54 of the 56 references it cites were found in a registry or an index.
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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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 the citing paper
“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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Cites
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Self-citationHow to Use a Monte Carlo Study to Decide on Sample Size and Determine Power
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Cited by
Sorted by citations
Sinha, P., Furfaro, D. M., Cummings, M. J., Abrams, D. C., Delucchi, K., Maddali, M. V., He, J., Thompson, A., Murn, M., Fountain, J. H., et al. (2021) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202105-1302oc · Cited by 294
Self-citationSinha, P., Kerchberger, V. E., Willmore, A., Chambers, J., Zhuo, H., Abbott, J. A., Jones, C., Wickersham, N. E., Wu, N., Neyton, L. P. A., et al. (2023) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(23)00237-0 · Cited by 235
Self-citationDahmer, M. K., Yang, G., Zhang, M., Quasney, M. W., Sapru, A., Weeks, H. M., Sinha, P., Curley, M. A. Q., Delucchi, K. L., Calfee, C. S., et al. (2021) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(21)00382-9 · Cited by 137
Self-citationBos, L. D. J., Sjoding, M. W., Sinha, P., Bhavani, S. V., Lyons, P. G., Bewley, A. F., Botta, M., Tsonas, A. M., Neto, A. S., Schultz, M. J., et al. (2021) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(21)00365-9 · Cited by 131
Self-citationSinha, P., Delucchi, K., Chen, Y., Zhuo, H., Abbott, J. A., Wang, C., Wickersham, N. E., McNeil, J. B., Jauregui, A., Ke, S., et al. (2021) · Thorax · doi:10.1136/thoraxjnl-2021-217158 · Cited by 120
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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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