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首页> 外文期刊>Journal of the royal statistical society >Multivariate latent variable transition models of longitudinal mixed data: an analysis on alcohol use disorder
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Multivariate latent variable transition models of longitudinal mixed data: an analysis on alcohol use disorder

机译：纵向混合数据的多元潜在变量转移模型：酒精使用障碍的分析

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Alcohol abuse is a dangerous habit in young people. The National Youth Survey is a longitudinal American study in part devoted to the investigation of alcohol disorder over time. The symptoms of alcohol disorder are measured by binary and ordinal items. In the literature it is well recognized that alcohol abuse can be measured by a latent construct; therefore generalized latent variable models for mixed data represent the ideal framework to analyse these data. However, it might be desirable to cluster individuals according to the different severity of their alcohol use disorder and to investigate how the groups vary over time. We present a new methodological framework that includes two levels of latent variables: one continuous hidden variable for dimension reduction and clustering and a discrete random variable accounting for the dynamics of alcohol disorder symptoms. The effect of covariates is also measured and a testing procedure for the temporal assumption is developed. This work addresses three important issues. First, it represents a unified framework for the analysis of longitudinal multivariate mixed data. Secondly, it captures and models the unobserved heterogeneity of the data. Finally it describes the dynamics of the data through the definition of latent constructs.
机译：酗酒是年轻人的一种危险习惯。全国青年调查是一项纵向的美国研究，部分致力于随着时间的推移对酒精中毒的调查。酒精中毒的症状通过二元和序数项目来衡量。在文献中，众所周知，酗酒可以通过潜在的手段来衡量。因此，用于混合数据的广义潜在变量模型代表了分析这些数据的理想框架。但是，可能需要根据他们的酒精使用障碍的不同严重程度对他们进行分组，并研究各组随时间变化的方式。我们提出了一个新的方法框架，其中包括两个级别的潜在变量：一个用于维数减少和聚类的连续隐藏变量，以及一个用于解释酒精中毒症状动态的离散随机变量。还测量了协变量的影响，并开发了针对时间假设的测试程序。这项工作解决了三个重要问题。首先，它代表了用于分析纵向多元混合数据的统一框架。其次，它捕获并建模了数据中未观察到的异质性。最后，它通过潜在构造的定义来描述数据的动态。

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