In precision medicine, a common question for researchers is whether patients can be classified with others who have similar risks and treatment responses. Such groupings can assist in predicting risk and matching patients with appropriate treatment strategies. The challenge is that it is often not easy to identify meaningful clusters of people with the observable data.
Latent class analysis (LCA) is a common explanatory modeling technique that allows researchers to identify groups of people who have similar characteristics that can include demographics, clinical characteristics, treatments, comorbidities, and outcomes.1 The term latent derives from the fact that the classes are not directly observable. Latent class analysis estimates the probability of each participant being a member of each latent class.2
Mori M, Krumholz HM, Allore HG. Using Latent Class Analysis to Identify Hidden Clinical Phenotypes. JAMA. 2020;324(7):700–701. doi:10.1001/jama.2020.2278
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