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

2019 ◽  
Vol 38 (23) ◽  
pp. 4702-4717 ◽  
Author(s):  
Cécile Proust‐Lima ◽  
Viviane Philipps ◽  
Jean‐François Dartigues
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Joint Model ◽  
Dynamic Processes ◽  
Clinical Endpoints

scholarly journals PRM24 - A JOINT MODEL OF COGNITIVE DECLINE AND TIME TO DIAGNOSIS TO IMPROVE PREDICTIONS OF THE RISK OF DEVELOPING ALZHEIMER’S DISEASE

2018 ◽  
Vol 21 ◽  
pp. S359-S360
Author(s):  
H Karcher ◽  
V Risson ◽  
G Lestini ◽  
N Coello ◽  
L Qi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Joint Model ◽  
Time To Diagnosis

VERUBECESTAT DISEASE PROGRESSION MODELING FROM APECS, A PHASE 3 TRIAL IN PRODROMAL ALZHEIMER’S DISEASE: NO DOSE- AND EXPOSURE-DEPENDENCY IN CLINICAL ENDPOINTS

2019 ◽  
Vol 15 (7) ◽  
pp. P249-P250
Author(s):  
Julie A. Stone ◽  
Huub Jan Kleijn ◽  
Ming Xu ◽  
James Kost ◽  
Christine Furtek ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Phase 3 ◽  
Clinical Endpoints ◽  
Prodromal Alzheimer’S Disease ◽  
Disease Progression Modeling

Simulating the outcome of amyloid treatments in Alzheimer’s Disease from multi-modal imaging and clinical data

2020 ◽  
Author(s):  
Clément Abi Nader ◽  
Nicholas Ayache ◽  
Giovanni B. Frisoni ◽  
Philippe Robert ◽  
Marco Lorenzi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Data ◽  
Treatment Strategies ◽  
Disease Evolution ◽  
Clinical Endpoints ◽  
Clinical Biomarkers ◽  
Optimal Intervention ◽  
Models Of Disease ◽  
The Impact

AbstractRecent failures of clinical trials in Alzheimer’s Disease underline the critical importance of identifying optimal intervention time to maximize cognitive benefit. While several models of disease progression have been proposed, we still lack quantitative approaches simulating the effect of treatment strategies on the clinical evolution. In this work, we present a data-driven method to model dynamical relationships between imaging and clinical biomarkers. Our approach allows simulating intervention at any stage of the pathology by modulating the progression speed of the biomarkers, and by subsequently assessing the impact on disease evolution. When applied to multi-modal imaging and clinical data from the Alzheimer’s Disease Neuroimaging Initiative our method enables to generate hypothetical scenarios of amyloid lowering interventions. Our results show that in a study with 1000 individuals per arm, accumulation should be completely arrested at least 5 years before Alzheimer’s dementia diagnosis to lead to statistically powered improvement of clinical endpoints.

P1-042: SUPPORTING DISEASE MODIFYING THERAPIES VIA ANALYSES OF THE LINK BETWEEN CLINICAL ENDPOINTS AND BIOMARKERS IN ALZHEIMER'S DISEASE CLINICAL TRIALS

2006 ◽  
Vol 14 (7S_Part_5) ◽  
pp. P283-P283
Author(s):  
Xiaopeng Miao ◽  
Ying Tian ◽  
John O'Gorman ◽  
Tianle Chen ◽  
Philip von Rosenstiel
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Clinical Endpoints ◽  
Disease Modifying Therapies ◽  
Disease Modifying

A Primer on the Evolution of Aducanumab: The First Antibody Approved for Treatment of Alzheimer’s Disease

2021 ◽  
pp. 1-16
Author(s):  
Sanchari Mukhopadhyay ◽  
Debanjan Banerjee
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Monoclonal Antibody ◽  
Cholesterol Metabolism ◽  
Neurodegenerative Disorder ◽  
Treatment Options ◽  
Surrogate Marker ◽  
Amyloid Β ◽  
Clinical Endpoints ◽  
Definition Of

Alzheimer’s disease (AD) is the most common form of dementia with global burden projected to triple by 2050. It incurs significant biopsychosocial burden worldwide with limited treatment options. Aducanumab is the first monoclonal antibody recently approved by the US-FDA for mild AD through the accelerated approval pathway. It is the first molecule to be approved for AD since 2003 and carries with it a therapeutic promise for the future. As the definition of AD has evolved from a pathological entity to a clinic-biological construct over the years, the amyloid-β (Aβ) pathway has been increasingly implicated in its pathogenesis. The approval of Aducanumab is based on reduction of the Aβ load in the brain, which forms a surrogate marker for this pathway. The research populace has, however, been globally divided by skepticism and hope regarding this approval. Failure to meet clinical endpoints in the trials, alleged transparency issues, cost-effectiveness, potential adverse effects, need for regular monitoring, and critique of ‘amyloid cascade hypothesis’ itself are the main caveats concerning the antibody. With this controversy in background, this paper critically looks at antibody research in AD therapeutics, evidence, and evolution of Aducanumab as a drug and the potential clinical implications of its use in future. While the efficacy of this monoclonal antibody in AD stands as a test of time, based on the growing evidence it is vital to rethink and explore alternate pathways of pathogenesis (oxidate stress, neuroinflammation, cholesterol metabolism, vascular factors, etc.) as possible therapeutic targets that may help elucidate the enigma of this complex yet progressive and debilitating neurodegenerative disorder.

scholarly journals Functional joint model for longitudinal and time-to-event data: an application to Alzheimer's disease

2017 ◽  
Vol 36 (22) ◽  
pp. 3560-3572 ◽  
Author(s):  
Kan Li ◽  
Sheng Luo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Joint Model ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

scholarly journals P4-558: VALIDATION OF AN ALGORITHM FOR PREDICTING TIME TO KEY CLINICAL ENDPOINTS IN PATIENTS WITH ALZHEIMER'S DISEASE

2019 ◽  
Vol 15 ◽  
pp. P1533-P1533
Author(s):  
Yaakov Stern ◽  
Eric Stallard
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Endpoints

scholarly journals Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer’s disease

2017 ◽  
Vol 28 (2) ◽  
pp. 327-342 ◽  
Author(s):  
Kan Li ◽  
Sheng Luo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Survival Data ◽  
Functional Data ◽  
Joint Modeling ◽  
Joint Model ◽  
Repeated Measurements ◽  
Joint Models ◽  
Modeling Framework ◽  
Dynamic Prediction

In the study of Alzheimer’s disease, researchers often collect repeated measurements of clinical variables, event history, and functional data. If the health measurements deteriorate rapidly, patients may reach a level of cognitive impairment and are diagnosed as having dementia. An accurate prediction of the time to dementia based on the information collected is helpful for physicians to monitor patients’ disease progression and to make early informed medical decisions. In this article, we first propose a functional joint model to account for functional predictors in both longitudinal and survival submodels in the joint modeling framework. We then develop a Bayesian approach for parameter estimation and a dynamic prediction framework for predicting the subjects’ future outcome trajectories and risk of dementia, based on their scalar and functional measurements. The proposed Bayesian functional joint model provides a flexible framework to incorporate many features both in joint modeling of longitudinal and survival data and in functional data analysis. Our proposed model is evaluated by a simulation study and is applied to the motivating Alzheimer’s Disease Neuroimaging Initiative study.

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