A prognostic model of Alzheimer's disease relying on multiple longitudinal measures and time‐to‐event data
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Introduction: Characterizing progression in Alzheimer's disease is critically important for early detection and targeted treatment. The objective was to develop a prognostic model, based on multivariate longitudinal markers, for predicting progression‐free survival in patients with mild cognitive impairment. Methods: The information contained in multiple longitudinal markers was extracted using multivariate functional principal components analysis and used as predictors in the Cox regression models. Cross‐validation was used for selecting the best model based on Alzheimer's Disease Neuroimaging Initiative–1. External validation was conducted on Alzheimer's Disease Neuroimaging Initiative–2. Results: Model comparison yielded a prognostic index computed as the weighted combination of historical information of five neurocognitive longitudinal markers that are routinely collected in observational studies. The comprehensive validity analysis provided solid evidence of the usefulness of the model for predicting Alzheimer's disease progression. Discussion: The prognostic model was improved by incorporating multiple longitudinal markers. It is useful for monitoring disease and identifying patients for clinical trial recruitment.
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Prior work has modeled time-to-AD-progression in a survival framework and obtained encouraging results.
“Several studies [11, 12, 16] have focused on the prediction of time of progression to AD under a time-to-event analysis setting, and promising performance have been obtained.”
MFPC scores have been used for longitudinal marker representation and missing/irregular data, but their latent-process assumption may be inappropriate for different marker types.
“Multivariate functional principal component (MFPC) scores [12] have been used to represent longitudinal makers for handling missing or irregular data. However, certain assumption is adopted in MFPC to model the latent longitudinal process, which may not be appropriate for different types of markers.”
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References 26 · Cited by 44
- Authors
- Li, K., O'Brien, R., Lutz, M., Luo, S., & Initiative, T. A. D. N.
- Affiliations
- The University of Texas Health Science Center at Houston (US)
- Department of Neurology Duke University School of Medicine Durham NC USA
- Duke University (US)
- Alzheimer’s Disease Neuroimaging Initiative (US)
- Corresponding author
- Luo, Sheng
- Venue
- Alzheimer's & Dementia
- Publisher
- Wiley
- Published
- 2018-01-04
- Volume
- 14
- Issue
- 5
- Pages
- 644-651
- ISSN
- 1552-5260 (ISSN-L)
- 1552-5279
- PMC
- PMC5938096
- OpenAlex
- W2782117342
- Cited by
- 44
- References
- 26
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 9 · 2023: 6 · 2022: 4 · 2021: 5 · 2020: 8 · 2019: 3 · 2018: 1
- FWCI
- 3.32
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Alzheimer s & Dementia (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS091307)
- Alzheimer's Disease Neuroimaging Initiative (U01 AG024904)
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- Canadian Institutes of Health Research
- Northern California Institute for Research and Education
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Cancer-related cognitive impairment studies
- Keywords
- Alzheimer's disease, mild cognitive impairment
- MeSH terms
- Aged, Alzheimer Disease, Brain, Female, Humans, Longitudinal Studies, Male, Neuropsychological Tests, Prognosis, Models, Statistical, Biomarkers, Disease Progression, Early Diagnosis, Neuroimaging, Cognitive Dysfunction
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