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

2020/11/08 by Pratik Sinha, Carolyn S. Calfee, Kevin L. Delucchi +1 · 32 citations
Mathematics · Medicine · #Data-Driven Disease Surveillance #Pneumonia and Respiratory Infections #Statistical Methods and Bayesian Inference

paper · doi:10.1097/ccm.0000000000004710

openalex publication_date 2020/11/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

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.

Citations

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