Estimating long‐term multivariate progression from short‐term data
Open accessAbstract
Motivation: Diseases that progress slowly are often studied by observing cohorts at different stages of disease for short periods of time. The Alzheimer's Disease Neuroimaging Initiative (ADNI) follows elders with various degrees of cognitive impairment, from normal to impaired. The study includes a rich panel of novel cognitive tests, biomarkers, and brain images collected every 6 months for as long as 6 years. The relative timing of the observations with respect to disease pathology is unknown. We propose a general semiparametric model and iterative estimation procedure to estimate simultaneously the pathological timing and long‐term growth curves. The resulting estimates of long‐term progression are fine‐tuned using cognitive trajectories derived from the long‐term “Personnes Agées Quid” study. Results: We demonstrate with simulations that the method can recover long‐term disease trends from short‐term observations. The method also estimates temporal ordering of individuals with respect to disease pathology, providing subject‐specific prognostic estimates of the time until onset of symptoms. When the method is applied to ADNI data, the estimated growth curves are in general agreement with prevailing theories of the Alzheimer's disease cascade. Other data sets with common outcome measures can be combined using the proposed algorithm. Availability: Software to fit the model and reproduce results with the statistical software R is available as the grace package. ADNI data can be downloaded from the Laboratory of NeuroImaging.
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A similar proportion of cognitively unimpaired elderly persons has abnormal amyloid biomarkers.
“and a similar proportion has abnormal amyloid biomarkers [33,53–55,60,70–73].”
From NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease (2018) · cites this paper as [72]
The ADNI and PAQUID results (section 5; page unavailable) identify 100 cognitively normal ADNI participants in an Amyloid+ group and 190 in an Amyloid− group. This supports the presence of abnormal amyloid biomarkers in some cognitively unimpaired participants, but the paper does not establish the stated proportion for cognitively unimpaired elderly people generally or specify what it is ‘similar’ to. (LLM, conf=0.82)
Experimental — accuracy not yet measuredA widely held view is that amyloid biomarkers represent the earliest evidence of AD neuropathologic change currently detectable in living persons.
“Consequently, a widely held view is that amyloid biomarkers represent the earliest evidence of AD neuropathologic change currently detectable in living persons [9,11,72,103,104].”
From NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease (2018) · cites this paper as [72]
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- Authors
- Donohue, M. C., Jacqmin‐Gadda, H., Le Goff, M., Thomas, R. G., Raman, R., Gamst, A. C., Beckett, L. A., Jack, C. R., Weiner, M. W., Dartigues, J., & Aisen, P. S.
- Affiliations
- University of California San Diego (US)
- Inserm (FR)
- INSERM, U897 Biostatistics Department Bordeaux France
- University of California, Davis (US)
- Mayo Clinic (US)
- University of California, San Francisco (US)
- Corresponding author
- Donohue, Michael C.
- ORCID
- Venue
- Alzheimer's & Dementia
- Publisher
- Wiley
- Published
- 2014-03-24
- Volume
- 10
- Issue
- 5S
- Pages
- S400-10
- ISSN
- 1552-5260 (ISSN-L)
- 1552-5279
- PMC
- PMC4169767
- OpenAlex
- W2110759468
- Cited by
- 197
- References
- 33
- Citations by year
- 2026: 8 · 2025: 18 · 2024: 16 · 2023: 15 · 2022: 15 · 2021: 24 · 2020: 18 · 2019: 25 · 2018: 24 · 2017: 13 · 2016: 9 · 2015: 9 · 2014: 3
- FWCI
- 8.65
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Alzheimer s & Dementia (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (1KL2RR031978)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- disease progression, Alzheimer's disease
- MeSH terms
- Algorithms, Alzheimer Disease, Computer Simulation, Humans, Models, Biological, Prognosis, Software, Multivariate Analysis, Databases, Factual, Disease Progression
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