ArticleJournal of Alzheimer's disease : JAD2017
Prediction of Conversion to Alzheimer's Disease with Longitudinal Measures and Time-To-Event Data.
Article in Journal of Alzheimer's disease : JAD, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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Who cites it
58 citing papers in PubMed.
- Trial
- A Dynamic Prediction Study of Persistent Nine-valent Human Papillomavirus Infection Among HIV-negative Men Who Have Sex With Men in Urumqi, China: a Longitudinal and Time-to-event-based Joint Model.Open forum infectious diseases · 2026Article
- Plasma lipid trajectories improve prediction of future Alzheimer's disease.Journal of Alzheimer's disease : JAD · 2026Article
- Regression analysis of interval-censored competing risks data with missing causes of failure: A direct likelihood approach.Statistical methods in medical research · 2026Article
- Rethinking on bile acid-brain axis: decoding neurotoxic and neuroprotective landscape in aging and Alzheimer's disease.Biogerontology · 2026Review
- Joint modeling approaches for predicting conversion from mild cognitive impairment to Alzheimer's disease: A systematic review (1999-2025).Belitung nursing journal · 2026Review
- Dynamic and concordance-assisted learning for risk stratification with application to Alzheimer's disease.Biostatistics (Oxford, England) · 2025Article
- Ensemble survival analysis for preclinical cognitive decline prediction in Alzheimer's disease using longitudinal biomarkers.Journal of Alzheimer's disease : JAD · 2025Article
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- A dynamic prediction model for predicting the time at which patients with MCI progress to AD based on time-dependent covariates.BMC medical informatics and decision making · 2025Article
- A Latent-Class Model for Time-To-Event Outcomes and High-Dimensional Imaging Data.Statistics in medicine · 2025Article
- Post-selection inference for the Cox model with interval-censored data.Scandinavian journal of statistics, theory and applications · 2025Article
- Integrative Multi-Omics and Multivariate Longitudinal Data Analysis for Dynamic Risk Estimation in Alzheimer's Disease.Statistics in medicine · 2025Article
- DYNAMIC PREDICTION WITH MULTIVARIATE LONGITUDINAL OUTCOMES AND LONGITUDINAL MAGNETIC RESONANCE IMAGING DATA.The annals of applied statistics · 2025Article
- Distinguishing Early from Late Mild Cognitive Impairment Using Magnetic Resonance Free-Water Diffusion Tensor Imaging.NeuroSci · 2025Article
- Article
- Individualized, cross-validated prediction of future dementia using cognitive assessments in people with mild cognitive symptoms.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- The sparse estimation of the semiparametric linear transformation model with dependent current status data.Journal of applied statistics · 2024Article
- Identifying underlying patterns in Alzheimer's disease trajectory: a deep learning approach and Mendelian randomization analysis.EClinicalMedicine · 2023Article
- BAYESIAN INFERENCE AND DYNAMIC PREDICTION FOR MULTIVARIATE LONGITUDINAL AND SURVIVAL DATA.The annals of applied statistics · 2023Article
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6 authors.
Funding
Abstract
backgroundIdentifying predictors of conversion to Alzheimer's disease (AD) is critically important for AD prevention and targeted treatment.
objectiveTo compare various clinical and biomarker trajectories for tracking progression and predicting conversion from amnestic mild cognitive impairment to probable AD.
methodsParticipants were from the ADNI-1 study. We assessed the ability of 33 longitudinal biomarkers to predict time to AD conversion, accounting for demographic and genetic factors. We used joint modelling of longitudinal and survival data to examine the association between changes of measures and disease progression. We also employed time-dependent receiver operating characteristic method to assess the discriminating capability of the measures.
results23 of 33 longitudinal clinical and imaging measures are significant predictors of AD conversion beyond demographic and genetic factors. The strong phenotypic and biological predictors are in the cognitive domain (ADAS-Cog; RAVLT), functional domain (FAQ), and neuroimaging domain (middle temporal gyrus and hippocampal volume). The strongest predictor is ADAS-Cog 13 with an increase of one SD in ADAS-Cog 13 increased the risk of AD conversion by 2.92 times.
conclusionPrediction of AD conversion can be improved by incorporating longitudinal change information, in addition to baseline characteristics. Cognitive measures are consistently significant and generally stronger predictors than imaging measures.
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