# Closing the gap between observational research and randomized trials for prevention of Alzheimer's Disease and dementia

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $770,404

## Abstract

Closing the gap between observational research and randomized trials for prevention of Alzheimer's
Disease and dementia
MPI: Glymour and Power in response to PAR-17-054.html
Summary
Launching randomized controlled trials (RCTs) for Alzheimer’s disease (AD) prevention is an urgent public
health priority. Although cardiovascular risk factor management is among the most promising intervention
strategies, there is considerable uncertainty about the optimal eligibility criteria, intervention details, duration, or
outcome assessments. Many major trials targeting AD prevention have been disappointing. One possible
reason for these disappointments is that observational research has not provided enough information to
anticipate whether a proposed RCT would succeed. Observational studies rarely specify populations,
exposures, and duration of follow-up with enough detail to guide RCT development. Most observational studies
do not have enough information to provide detailed guidance for RCT development. Integration across
heterogeneous observational data sources is necessary to achieve the sample size, diversity, and variety of
measurements necessary to guide RCT development. In other research areas, simulations have proven useful
tools to combine diverse sources of evidence, but in AD prevention, we currently lack tools to systematically
combine evidence from heterogeneous data sources in order to guide trial design. This proposal takes
advantage of recent advances in causal methods for data integration to overcome the previous barriers and
develop a simulation model leveraging all of the information from diverse data sources, including cohorts,
clinical administrative data, and registry information. In AIM 1, we combine information from 8 observational
studies, including cohorts, biobanks, and registries, into a unified, flexible, prevention simulation model. This
model can simulate effects of hypothetical trials and thereby provide specific guidance for development of
effective RCTs for AD prevention. We begin by estimating a structural model using data from the
Cardiovascular Health Study (CHS, n=5,888) and the Atherosclerosis Risk in Communities (ARIC, n=15,792)
cohorts, which include detailed exposure, outcome, and covariate measures. We will then incorporate data
from 6 other sources, with information in total on 1.6 million individuals. We will use a latent variable approach
to incorporate alternative measures of exposures, outcomes, and covariates. In AIM 2, the prevention
simulation model will be tested, refined, and validated by comparing simulated and actual findings of the
ACCORD-MIND, ACCORDION-MIND, SYST-EUR, HYVET-COG, SCOPE, SHEP, and SPRINT-MIND trials.
AIM 3 will compare a range of hypothetical trials for diabetes and hypertension management to identify
interventions most likely to succeed, considering eligibility criteria, intensity and duration of intervention, and
outcome measures. In AIM 4 we develop user-friendly interfaces for the mo...

## Key facts

- **NIH application ID:** 10168409
- **Project number:** 5R01AG057869-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Medellena Maria Glymour
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $770,404
- **Award type:** 5
- **Project period:** 2018-09-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10168409

## Citation

> US National Institutes of Health, RePORTER application 10168409, Closing the gap between observational research and randomized trials for prevention of Alzheimer's Disease and dementia (5R01AG057869-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10168409. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*