A joint model for multiple dynamic processes and clinical endpoints: application to Alzheimer's disease
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Alzheimer's disease, the most frequent dementia in the elderly, is characterized by multiple progressive impairments in the brain structure and in clinical functions such as cognitive functioning and functional disability. Until recently, these components were mostly studied independently while they are fundamentally inter-related in the degradation process towards dementia. We propose a joint model to describe the dynamics of multiple correlated latent processes which represent various domains impaired in the Alzheimer's disease. Each process is measured by one or several markers, possibly non Gaussian. Rather than considering the associated time to dementia as in standard joint models, we assume dementia diagnosis corresponds to the passing above a covariate-specific threshold of a pathological process modelled as a combination of the domain-specific latent processes. This definition captures the clinical complexity of dementia diagnosis but also benefits from simplifications for the obtention of Maximum Likelihood Estimates. We show that the model and estimation procedure can also handle competing clinical endpoints such as death. The estimation procedure is validated by simulations and the method is illustrated on a large French population-based cohort of cerebral aging with a maximum follow-up of 25 years. We focused on three clinical manifestations (cognitive functioning, physical dependency and depressive symptoms) measured repeatedly by one or several markers, as well as repeated clinical-based diagnoses of dementia and competing death before dementia.
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