The Methodology Center, Penn State, 404 Health & Human Development Bldg., University Park, PA, 16802, USA.
College of Health and Human Development, Penn State, 304 Health & Human Development Bldg., University Park, PA, 16802, USA.
Prev Sci. 2019 Apr;20(3):394-406. doi: 10.1007/s11121-018-0883-8.
Latent class analysis (LCA) has proven to be a useful tool for identifying qualitatively different population subgroups who may be at varying levels of risk for negative outcomes. Recent methodological work has improved techniques for linking latent class membership to distal outcomes; however, these techniques do not adjust for potential confounding variables that may provide alternative explanations for observed relations. Inverse propensity score weighting provides a way to account for many confounders simultaneously, thereby strengthening causal inference of the effects of predictors on outcomes. Although propensity score weighting has been adapted to LCA with covariates, there has been limited work adapting it to LCA with distal outcomes. The current study proposes a step-by-step approach for using inverse propensity score weighting together with the "Bolck, Croon, and Hagenaars" approach to LCA with distal outcomes (i.e., the BCH approach), in order to estimate the causal effects of reasons for alcohol use latent class membership during the year after high school (at age 19) on later problem alcohol use (at age 35) with data from the longitudinal sample in the Monitoring the Future study. A supplementary appendix provides evidence for the accuracy of the proposed approach via a small-scale simulation study, as well as sample programming code to conduct the step-by-step approach.
潜在类别分析(LCA)已被证明是一种有用的工具,可以识别出在负面结果风险方面可能处于不同水平的具有不同性质的人群亚组。最近的方法工作改进了将潜在类别成员与远端结果联系起来的技术;然而,这些技术并没有调整可能提供观察到的关系的替代解释的潜在混杂变量。逆倾向评分加权提供了一种同时考虑许多混杂因素的方法,从而加强了对预测因素对结果的影响的因果推断。尽管已经将倾向评分加权应用于具有协变量的潜在类别分析,但将其应用于具有远端结果的潜在类别分析的工作有限。本研究提出了一种使用逆倾向评分加权与“Bolck、Croon 和 Hagenaars”方法相结合的逐步方法,以估计高中毕业后一年(19 岁)时饮酒原因潜在类别成员身份对后来问题饮酒(35 岁)的因果效应,该方法使用了来自未来监测研究中纵向样本的数据。补充附录通过小规模模拟研究提供了对所提出方法准确性的证据,以及用于逐步方法的样本编程代码。