Lanza Stephanie T, Bray Bethany C
The Methodology Center, The Pennsylvania State University.
Adv Appl Stat Sci. 2010 Oct;3(2):203-235.
Latent class analysis (LCA) is a statistical approach to identifying underlying subgroups (i.e. latent classes) of individuals based on their responses to a set of observed categorical variables. Latent transition analysis (LTA) extends this framework to longitudinal data in order to estimate the incidence of transitions over time in latent class membership. This study provides an introduction to LCA and LTA, including the use of grouping variables and covariates, and demonstrates the use of two SAS ® procedures (PROC LCA and PROC LTA) to fit these models. The empirical demonstration involved data from 457 women who participated in the Women's Interagency HIV Study (WIHS). First, LCA was used to identify drug use latent classes based on reported use of tobacco, alcohol, marijuana, crack/cocaine/heroin and other drugs. Second, LTA was used to estimate the incidence of transitions in drug use latent classes over a one-year period. Third, racial differences in initial drug use and transitions over time were examined using multiple-groups LTA. Fourth, the effect of participation in an alcohol or drug treatment program on initial latent class membership and transitions over time were examined using LTA with covariates. Measurement invariance across time and groups is examined.
潜在类别分析（LCA）是一种统计方法，用于根据个体对一组观察到的分类变量的反应来识别潜在的亚组（即潜在类别）。潜在转变分析（LTA）将此框架扩展到纵向数据，以估计潜在类别成员身份随时间转变的发生率。本研究介绍了LCA和LTA，包括分组变量和协变量的使用，并演示了使用两个SAS®程序（PROC LCA和PROC LTA）来拟合这些模型。实证演示涉及来自457名参与妇女机构间HIV研究（WIHS）的女性的数据。首先，LCA用于根据报告的烟草、酒精、大麻、快克/可卡因/海洛因及其他药物的使用情况来识别药物使用潜在类别。其次，LTA用于估计一年内药物使用潜在类别转变的发生率。第三，使用多组LTA检查初始药物使用和随时间转变的种族差异。第四，使用带有协变量的LTA检查参与酒精或药物治疗项目对初始潜在类别成员身份和随时间转变的影响。研究了时间和组间的测量不变性。
