Improving prediction and understanding of Alzheimer's using long-term data
Statistical Methods for Accurate Estimation and Prediction in Alzheimer's Disease
This project creates improved statistical tools to make better predictions about Alzheimer's progression and to help design more effective trials for people at risk of or living with Alzheimer's.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | New York University NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project ID | NIH-11330675 on NIH RePORTER |
What this research studies
From a patient's perspective, researchers are building new math tools to correct biases in long-term Alzheimer's datasets so timing of symptoms is more meaningful. They will adjust for how and when people join or leave studies (complex truncation and censoring) so estimates reflect real clinical timelines like onset of cognitive decline or mild cognitive impairment. The work focuses on re-analyzing existing cohort data to produce more accurate risk estimates and individual predictions. Rather than testing drugs, the deliverables are methods that other researchers can use to plan trials and give clearer personal risk information.
Who could benefit from this research
Good fit: People with mild cognitive impairment, early Alzheimer's, or older adults enrolled in long-term memory and dementia cohorts are the kinds of patients whose data this work aims to improve.
Not a fit: Patients without longitudinal follow-up data, with non-Alzheimer's dementias, or seeking immediate drug treatments are unlikely to receive direct benefit from this methods-focused work.
Why it matters
Potential benefit: If successful, this could make individual risk forecasts more accurate and help design clinical trials that find effective treatments faster.
How similar studies have performed: Related statistical methods for censoring and truncation have helped other diseases, but applying these specific adjustments to Alzheimer's longitudinal cohorts and trial planning is relatively novel.
Where this research is happening
New York, United States
- New York University — New York, United States (Active)
Researchers
- Principal investigator: Betensky, Rebecca a. — New York University
- Study coordinator: Betensky, Rebecca a.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.