ArticleJournal of Alzheimer's disease : JAD2020
Machine Learning Predictive Models Can Improve Efficacy of Clinical Trials for Alzheimer's Disease.
Article in Journal of Alzheimer's disease : JAD, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 53 citations in OpenAlex.
- How to improve statistical power in a trial with SCA2 patients using natural history data.Trials · 2026Article
- Identifying Alzheimer's Disease Progression Subphenotypes Via a Graph-based Framework Using Electronic Health Records.Journal of healthcare informatics research · 2026Article
- A vision-language foundation model for Alzheimer's disease diagnosis using MRI and clinical data.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Prediction-powered inference for clinical trials: application to linear covariate adjustment.BMC medical research methodology · 2025Article
- Prediction-powered Inference for Clinical Trials: application to linear covariate adjustment.medRxiv : the preprint server for health sciences · 2025Article
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- The Potential of Disease Progression Modeling to Advance Clinical Development and Decision Making.Clinical pharmacology and therapeutics · 2025Review
- Machine learning applications in healthcare clinical practice and research.World journal of clinical cases · 2025Article
- Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Review
- Harnessing artificial intelligence to improve clinical trial design.Communications medicine · 2023Article
- Article
- Article
- Monte Carlo cross-validation for a study with binary outcome and limited sample size.BMC medical informatics and decision making · 2022Article
- The Road to Personalized Medicine in Alzheimer's Disease: The Use of Artificial Intelligence.Biomedicines · 2022Review
- Influence of Subject-Specific Effects in Longitudinal Modelling of Cognitive Decline in Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2022Article
- Application of predictive models in boosting power of Alzheimer's disease clinical trials: A post hoc analysis of phase 3 solanezumab trials.Alzheimer's & dementia (New York, N. Y.) · 2022Article
- Predictive value of ATN biomarker profiles in estimating disease progression in Alzheimer's disease dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2021Article
- Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review.JAMIA open · 2021Review
- Quantifying the heterogeneity of cognitive functioning in Alzheimer's disease to extend the placebo-treatment dichotomy: Latent class analysis of individual-participant data from five pivotal randomized clinical trials of donepezil.European psychiatry : the journal of the Association of European Psychiatrists · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe ideal participants for Alzheimer's disease (AD) clinical trials would show cognitive decline in the absence of treatment (i.e., placebo arm) and also would be responsive to the therapeutic intervention being studied (i.e., drug arm). One strategy to boost the power of trials is to enroll individuals who are more likely to progress targeted using data-driven predictive models.
objectiveTo investigate if machine learning (ML) models can effectively predict clinical disease progression (cognitive decline) in mild-to-moderate AD patients during the timeframe of a phase III clinical trial.
methodsData from 202 participants with a diagnosis of AD at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) was used to train ML classifiers that can differentiate between individuals who had declining cognitive function (DC) and individuals with stable cognitive function (SC). DC was defined as any downward change in the Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-cog) score over 12 months of follow-up. SC was defined by the absence of decline in ADAS-cog. Trained models were applied to data from 77 participants from the placebo arm of the phase III trial of Semagacestat (LFAN study) to identify subgroups of SC versus DC.
resultsOnly 74.8% of ADNI participants and 63.6% of LFAN participants had cognitive decline after one year of follow up. K-nearest neighbors (kNN) classifier had an accuracy of 68.3%, sensitivity of 80.1%, and specificity of 33.3% for identifying decliners in ADNI (training sample). In LFAN (validation sample), the model showed an overall accuracy of 61.3%, sensitivity of 65.5%, and specificity of 47.0% in identifying decliners at the 12 months of follow-up. The model had a positive predictive value of 80.8%, which was 17.2% more than the base prevalence of decliners.
conclusionsMachine learning predictive models can be effectively used to boost the power of clinical trials by reducing the sample size.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.