Open AccessPractitioner’s Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls
Author(s) -
Pratik Sinha,
Carolyn S. Calfee,
Kevin Delucchi
Publication year - 2020
Publication title -
critical care medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.002
H-Index - 271
eISSN - 1530-0293
pISSN - 0090-3493
DOI - 10.1097/ccm.0000000000004710
Subject(s) - latent class model , probabilistic latent semantic analysis , class (philosophy) , medicine , inference , data science , cluster analysis , probabilistic logic , data mining , machine learning , artificial intelligence , computer science
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.