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In health cohort studies, repeated measures of markers are often used todescribe the natural history of a disease. Joint models allow to study theirevolution by taking into account the possible informative dropout usually dueto clinical events. However, joint modeling developments mostly focused oncontinuous Gaussian markers while, in an increasing number of studies, theactual quantity of interest is non-directly measurable; it constitutes a latentvariable evaluated by a set of observed indicators from questionnaires ormeasurement scales. Classical examples include anxiety, fatigue, cognition. Inthis work,we explain how joint models can be extended to the framework of alatent quantity measured over time by indicators of different nature (e.g.continuous, binary, ordinal). The longitudinal submodel describes the evolutionover time of the quantity of interest defined as a latent process in astructural mixed model, and links the latent process to each observation of theindicators through appropriate measurement models. Simultaneously, the risk ofmulti-cause event is modelled via a proportional cause-specific hazard modelthat includes a function of the mixed model elements as linear predictor totake into account the association between the latent process and the risk ofevent. Estimation, carried out in the maximum likelihood framework andimplemented in the R-package JLPM, has been validated by simulations. Themethodology is illustrated in the French cohort on Multiple-System Atrophy(MSA), a rare and fatal neurodegenerative disease, with the study of dysphagiaprogression over time stopped by the occurrence of death.

Joint models for the longitudinal analysis of measurement scales in the presence of informative dropout