A joint model for multiple dynamic processes and clinical endpoints: Application to Alzheimer's disease

Abstract

As other neurodegenerative diseases, 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 because no joint model for multivariate longitudinal data and time to event was available in the statistical community. Yet, these components are fundamentally interrelated in the degradation process toward dementia and should be analyzed together. We thus propose a joint model to simultaneously describe the dynamics of multiple correlated components. Each component, defined as a latent process, is measured by one or several continuous markers (not necessarily Gaussian). Rather than considering the associated time to diagnosis as in standard joint models, we assume diagnosis corresponds to the passing above a covariate‐specific threshold (to be estimated) of a pathological process that is modeled as a combination of the component‐specific latent processes. This definition captures the clinical complexity of diagnoses such as dementia diagnosis but also benefits from simplifications for the computation of maximum likelihood estimates. We show that the model and estimation procedure can also handle competing clinical endpoints. The estimation procedure, implemented in an R package, is validated by simulations and the method is illustrated on a large French population‐based cohort of cerebral aging in which we focused on the dynamics of three clinical manifestations and the associated risk of dementia and death before dementia.

Progression models for repeated measures: Estimating novel treatment effects in progressive diseases

“…This has been a major focus in Alzheimer's disease, where a range of exploratory disease progression models with latent variables describing patient-level progression has been developed with a focus on predicting the disease stage and future decline of an individual patient. [30][31][32][33][34][35] Future work should address the most appropriate use of the PMRM methodology in relation to covariates, random effects and multivariate outcomes in clinical trials.…”

Disease Modelling of Cognitive Outcomes and Biomarkers in the European Prevention of Alzheimer’s Dementia Longitudinal Cohort

“…Various disease progression and sub-type approaches have been proposed and developed. These include survival and multi-state models for investigating transitions between disease states ( Hubbard and Zhou, 2011 ; Vos et al, 2013 ; van den Hout, 2016 ; Wei and Kryscio, 2016 ; Robitaille et al, 2018 ; Zhang et al, 2019 ); mixed effects models (linear, generalized, non-linear) that incorporate subject-specific random effects and can be extended to handle latent time shifts, random change points, latent factors, processes and classes, hidden states, and multiple outcomes ( Hall et al, 2000 ; Jedynak et al, 2012 ; Liu et al, 2013 ; Proust-Lima et al, 2013 ; Donohue et al, 2014 ; Samtani et al, 2014 ; Lai et al, 2016 ; Zhang et al, 2016 ; Geifman et al, 2018 ; Li et al, 2018 ; Wang et al, 2018 ; Lorenzi et al, 2019 ; Proust-Lima et al, 2019 ; Villeneuve et al, 2019 ; Younes et al, 2019 ; Bachman et al, 2020 ; Kulason et al, 2020 ; Raket, 2020 ; Segalas et al, 2020 ; Williams et al, 2020 ) and can be combined with models for event-history data ( Marioni et al, 2014 ; Blanche et al, 2015 ; Proust-Lima et al, 2016 ; Rouanet et al, 2016 ; Li et al, 2017 ; Iddi et al, 2019 ; Li and Luo, 2019 ; Wu et al, 2020 ); event-based models which attempt to model the pathological cascade of events occurring as the disease develops and progresses through disease stages ( Fonteijn et al, 2012 ; Young et al, 2014 ; Chen et al, 2016 ; Goyal et al, 2018 ; Oxtoby et al, 2018 ); and various clustering approaches for discovering risk stratification/disease progression groups and endotypes. For example, those based on hierarchical, partitioning and model-based clustering algorithms/methods ( Dong et al, 2016 ; Racine et al, 2016 ; Dong et al, 2017 ; ten Kate et al, 2018 ; Young et al, 2018 ).…”

An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries

“…In developing countries, even the big cities still have so many problems, let alone small cities in remote areas. It is usually more acute that the ratio of urban to rural population is about 4 : 6 in 2017 [ 14 ], but the city's medical level is much higher than that in the rural.…”