Aflaki Kayvan, Vigod Simone, Ray Joel G
Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.
Department of Psychiatry, Women's College Hospital, Toronto, Ontario, Canada.
J Clin Epidemiol. 2023 Jul;159:348-351. doi: 10.1016/j.jclinepi.2023.05.025. Epub 2023 Jun 5.
Latent class analysis (LCA) is an analytical approach for the identification of more homogeneous subgroups within an otherwise dissimilar patient population. In the current paper, Part II, we present a practical step-by-step guide for LCA of clinical data, including when LCA might be applied, selecting indicator variables, and choosing a final class solution. We also identify common pitfalls of LCA, and related solutions.
潜在类别分析（LCA）是一种用于在原本不同的患者群体中识别更具同质性亚组的分析方法。在本文的第二部分，我们为临床数据的潜在类别分析提供了一份实用的分步指南，包括潜在类别分析何时适用、选择指标变量以及选择最终的类别解决方案。我们还指出了潜在类别分析的常见陷阱及相关解决方法。
