Abstract
This article provides an overview of latent class analysis (LCA) and its applications in the field of health sciences. LCA is a statistical method used to identify unobserved subpopulations, or latent classes, within a population based on a set of categorical observed variables. In health sciences, LCA has been used in a variety of applications including identifying subtypes of disease, evaluating treatment effectiveness, and understanding health-related behaviors. The advantages and limitations of LCA are discussed, as well as the methodologies used in LCA. The article also provides examples of LCA applications in health sciences, including identifying subtypes of mental disorders, evaluating treatments for chronic diseases, and understanding health-related behaviors. Additionally, current research trends and future directions for the use of LCA in medicine are discussed.
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