ArticleAlzheimer's research & therapy2017
Personalized predictive modeling for patients with Alzheimer's disease using an extension of Sullivan's life table model.
Article in Alzheimer's research & therapy, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 31 citations in OpenAlex.
- Article
- Differences in the Distribution of Aβ in the Brain between U.S. Veterans and Adults aged 62+ and suffering from Alzheimer's Disease.Annals of biostatistics & biometric applications · 2024Article
- Life expectancy and healthy life expectancy of Korean registered disabled by disability type in 2014-2018: Korea National Rehabilitation Center database.BMC public health · 2023Article
- Associations between cognitive function and a range of significant life events in an elderly essential tremor cohort study.Journal of the neurological sciences · 2023Article
- Validation of a Multivariate Prediction Model of the Clinical Progression of Alzheimer's Disease in a Community-Dwelling Multiethnic Cohort.Journal of Alzheimer's disease : JAD · 2023Article
- Targeted Screening for Alzheimer's Disease Clinical Trials Using Data-Driven Disease Progression Models.Frontiers in artificial intelligence · 2022Article
- Validation and demonstration of a new comprehensive model of Alzheimer's disease progression.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2021Article
- Understanding and predicting the longitudinal course of dementia.Current opinion in psychiatry · 2019Review
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3 authors at 3 institutions in 1 country.
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Abstract
backgroundAlzheimer's disease (AD) progression varies substantially among patients, hindering calculation of residual total life expectancy (TLE) and its decomposition into disability-free life expectancy (DFLE) and disabled life expectancy (DLE) for individual patients with AD. The objective of the present study was to assess the accuracy of a new synthesis of Sullivan's life table (SLT) and longitudinal Grade of Membership (L-GoM) models that estimates individualized TLEs, DFLEs, and DLEs for patients with AD. If sufficiently accurate, such information could enhance the quality of important decisions in AD treatment and patient care.
methodsWe estimated a new SLT/L-GoM model of the natural history of AD over 10 years in the Predictors 2 Study cohort: N = 229 with 6 fixed and 73 time-varying covariates over 21 examinations covering 11 measurement domains including cognitive, functional, behavioral, psychiatric, and other symptoms/signs. Total remaining life expectancy was censored at 10 years. Disability was defined as need for full-time care (FTC), the outcome most strongly associated with AD progression. All parameters were estimated via weighted maximum likelihood using data-dependent weights designed to ensure that the estimates of the prognostic subtypes were of high quality. Goodness of fit was tested/confirmed for survival and FTC disability for five relatively homogeneous subgroups defined to cover the range of patient outcomes over the 21 examinations.
resultsThe substantial heterogeneity in initial patient presentation and AD progression was captured using three clinically meaningful prognostic subtypes and one terminal subtype exhibiting highly differentiated symptom severity on 7 of the 11 measurement domains. Comparisons of the observed and estimated survival and FTC disability probabilities demonstrated that the estimates were accurate for all five subgroups, supporting their use in AD life expectancy calculations. Mean 10-year TLE differed widely across subgroups: range 3.6-8.0 years, average 6.1 years. Mean 10-year DFLE differed relatively even more widely across subgroups: range 1.2-6.5 years, average 4.0 years. Mean 10-year DLE was relatively much closer: range 1.5-2.3 years, average 2.1 years.
conclusionsThe SLT/L-GoM model yields accurate maximum likelihood estimates of TLE, DFLE, and DLE for patients with AD; it provides a realistic, comprehensive modeling framework for endpoint and resource use/cost calculations.
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