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

作者：Pratik Sinha, Carolyn S. Calfee, Kevin Delucchi · 发表于：Critical Care Medicine · 年份：2020 · DOI：10.1097/ccm.0000000000004710 · 被引用次数：1165 · 研究领域：Statistical Methods and Bayesian Inference、Data-Driven Disease Surveillance、Pneumonia and Respiratory Infections

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.