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Propensity Score Analysis with Survey Weighted Data.使用调查加权数据的倾向得分分析。
J Causal Inference. 2015 Sep;3(2):237-249. doi: 10.1515/jci-2014-0039. Epub 2015 May 14.
2
Age-Related Changes in Associations Between Reasons for Alcohol Use and High-Intensity Drinking Across Young Adulthood.青年成年期饮酒原因与高强度饮酒之间关联的年龄相关变化
J Stud Alcohol Drugs. 2017 Jul;78(4):558-570. doi: 10.15288/jsad.2017.78.558.
3
Comparing the Performance of Improved Classify-Analyze Approaches For Distal Outcomes in Latent Profile Analysis.比较潜在剖面分析中用于远端结果的改进分类分析方法的性能。
Methodology (Gott). 2016 Oct;12(4):107-116. doi: 10.1027/1614-2241/a000114. Epub 2016 Dec 5.
4
Reasons for Marijuana Use Among Young Adults and Long-Term Associations With Marijuana Use and Problems.年轻人使用大麻的原因以及与大麻使用及其问题的长期关联。
J Stud Alcohol Drugs. 2016 Nov;77(6):881-888. doi: 10.15288/jsad.2016.77.881.
5
What Constitutes High-Quality Implementation of SEL Programs? A Latent Class Analysis of Second Step® Implementation.什么构成了社会情感学习(SEL)项目的高质量实施?对“第二步”®实施情况的潜在类别分析
Prev Sci. 2016 Nov;17(8):981-991. doi: 10.1007/s11121-016-0670-3.
6
Substance Use Disorder in Early Midlife: A National Prospective Study on Health and Well-Being Correlates and Long-Term Predictors.中年早期的物质使用障碍:一项关于健康与幸福的关联因素及长期预测因素的全国前瞻性研究。
Subst Abuse. 2016 May 25;9(Suppl 1):41-57. doi: 10.4137/SART.S31437. eCollection 2015.
7
Drinking to Cope: a Latent Class Analysis of Coping Motives for Alcohol Use in a Large Cohort of Adolescents.借酒消愁:对大量青少年群体饮酒应对动机的潜在类别分析
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8
Stress and Multiple Substance Use Behaviors Among Hispanic Adolescents.西班牙裔青少年中的压力与多种物质使用行为
Prev Sci. 2016 Feb;17(2):208-17. doi: 10.1007/s11121-015-0603-6.
9
Eliminating Bias in Classify-Analyze Approaches for Latent Class Analysis.消除潜在类别分析中分类-分析方法的偏差。
Struct Equ Modeling. 2015 Jan;22(1):1-11. doi: 10.1080/10705511.2014.935265.
10
ADDRESSING CONFOUNDING WHEN ESTIMATING THE EFFECTS OF LATENT CLASSES ON A DISTAL OUTCOME.在估计潜在类别对远端结局的影响时处理混杂因素
Health Serv Outcomes Res Methodol. 2014 Dec;14(4):232-254. doi: 10.1007/s10742-014-0122-0.

采用潜在类别暴露的逆倾向评分加权法:估计报告的饮酒原因对 16 年后问题性饮酒的因果效应。

Inverse Propensity Score Weighting with a Latent Class Exposure: Estimating the Causal Effect of Reported Reasons for Alcohol Use on Problem Alcohol Use 16 Years Later.

机构信息

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.

出版信息

Prev Sci. 2019 Apr;20(3):394-406. doi: 10.1007/s11121-018-0883-8.

DOI:10.1007/s11121-018-0883-8
PMID:29542004
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6139077/
Abstract

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.

摘要

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。