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

Pratik Sinha, Carolyn S. Calfee, Kevin L. Delucchi

Research output: Contribution to journalReview articlepeer-review

5 Scopus citations

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.

Original languageEnglish
Pages (from-to)E63-E79
JournalCritical care medicine
DOIs
StateAccepted/In press - 2020

Keywords

  • clustering algorithms
  • data science
  • heterogeneity
  • latent class analysis
  • phenotypes

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