In health cohort studies, repeated measures of markers are often used to describe the natural history of a disease. Joint models allow to study their evolution by taking into account the possible informative dropout usually due to clinical events. However, joint modeling developments mostly focused on continuous Gaussian markers while, in an increasing number of studies, the actual quantity of interest is non-directly measurable; it constitutes a latent variable evaluated by a set of observed indicators from questionnaires or measurement scales. Classical examples include anxiety, fatigue, cognition. In this work,we explain how joint models can be extended to the framework of a latent quantity measured over time by indicators of different nature (e.g. continuous, binary, ordinal). The longitudinal submodel describes the evolution over time of the quantity of interest defined as a latent process in a structural mixed model, and links the latent process to each observation of the indicators through appropriate measurement models. Simultaneously, the risk of multi-cause event is modelled via a proportional cause-specific hazard model that includes a function of the mixed model elements as linear predictor to take into account the association between the latent process and the risk of event. Estimation, carried out in the maximum likelihood framework and implemented in the R-package JLPM, has been validated by simulations. The methodology is illustrated in the French cohort on Multiple-System Atrophy (MSA), a rare and fatal neurodegenerative disease, with the study of dysphagia progression over time stopped by the occurrence of death.
翻译:在保健组群研究中,反复测量标记往往用于描述疾病自然史。联合模型允许研究其演变过程,考虑到通常由于临床事件而可能出现的信息性失学现象。然而,联合模型的发展主要侧重于连续高斯标记,而在越来越多的研究中,兴趣的实际数量是非直接测量的;它构成了由一组从调查问卷或测量尺度观察到的指标所评估的潜在变量;典型的例子包括焦虑、疲劳、认知。在这项工作中,我们解释如何将联合模型扩展至由不同性质指标(如连续、二进制、或定式)测量的一段时间内潜在数量框架。 纵向子模型描述了在结构混合模型中被界定为潜在过程的利息数量的演变,并将潜在过程与指标的每一项观察通过适当的测量模型进行联系。同时,多重原因事件的风险通过一个比例性、具体原因的危害模型模型模型模型模型模型,包括线性预测器功能,以考虑到由不同性质的指标性指标性指标性变化发生过程和事件发生前期(如连续、二进制、硬度)之间的潜在变化。在法国的模拟研究中,A-Rimal-Limation Forimal Frolate From From From From From Fr 中,采用了一个最有可能进行。