Part I: A friendly introduction to latent class analysis
- PMID: 35636591
- DOI: 10.1016/j.jclinepi.2022.05.008
Part I: A friendly introduction to latent class analysis
Abstract
Latent class analysis (LCA) offers a powerful analytical approach for categorizing groups (or "classes") within a heterogenous population. LCA identifies these hidden classes by a set of predefined features, known as "indicators". Unlike many other grouping analytical approaches, LCA derives classes using a probabilistic approach. In this first paper, we describe the common applications of LCA, and outline its advantages over other analytical subgrouping methods.
Keywords: Aikake information criterion; Bayesian information criterion; Latent class analysis; Mixture modeling; Model-based clustering; Subgroup analysis; Unsupervised methods.
Copyright © 2022 Elsevier Inc. All rights reserved.
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