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首页> 外文期刊>Journal of applied statistics >Dynamic latent trait models with mixed hidden Markov structure for mixed longitudinal outcomes
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Dynamic latent trait models with mixed hidden Markov structure for mixed longitudinal outcomes

机译：混合隐马尔可夫结构的动态潜在性状模型用于混合纵向结果

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摘要

We propose a general Bayesian joint modeling approach to model mixed longitudinal outcomes from the exponential family for taking into account any differential misclassification that may exist among categorical outcomes. Under this framework, outcomes observed without measurement error are related to latent trait variables through generalized linear mixed effect models. The misclassified outcomes are related to the latent class variables, which represent unobserved real states, using mixed hidden Markov models (MHMMs). In addition to enabling the estimation of parameters in prevalence, transition and misclassification probabilities, MHMMs capture cluster level heterogeneity. A transition modeling structure allows the latent trait and latent class variables to depend on observed predictors at the same time period and also on latent trait and latent class variables at previous time periods for each individual. Simulation studies are conducted to make comparisons with traditional models in order to illustrate the gains from the proposed approach. The new approach is applied to data from the Southern California Children Health Study to jointly model questionnaire-based asthma state and multiple lung function measurements in order to gain better insight about the underlying biological mechanism that governs the inter-relationship between asthma state and lung function development.
机译：我们提出一种通用的贝叶斯联合建模方法，以对指数族的混合纵向结果进行建模，以考虑到分类结果之间可能存在的任何不同的错误分类。在此框架下，通过广义线性混合效应模型，观察到的无测量误差的结果与潜在性状变量相关。错误分类的结果与使用混合隐马尔可夫模型（MHMM）的潜在类别变量有关，这些潜在类别变量表示未观察到的真实状态。除了能够估计患病率，转移率和分类错误率中的参数外，MHMM还捕获群集级别的异质性。过渡建模结构允许潜在特征和潜在类别变量在同一时间段依赖于观察到的预测变量，并且还取决于每个人在先前时间段的潜在特征和潜在类别变量。进行仿真研究以与传统模型进行比较，以说明所提方法的收益。该新方法应用于来自南加州儿童健康研究的数据，以联合建模基于问卷的哮喘状态和多种肺功能测量，以便更好地了解控制哮喘状态与肺功能之间相互关系的潜在生物学机制。发展。

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