# Improving causal inference in Alzheimer's Disease prevention research on modifiable risk factors: the Triangulation of Innovative Methods to EndAD (TIME-AD) project

> **NIH NIH P01** · BOSTON UNIVERSITY MEDICAL CAMPUS · 2024 · $5,262,301

## Abstract

OVERALL COMPONENT PROJECT SUMMARY
Research on Alzheimer’s Disease and Alzheimer’s Disease Related Dementias (AD/ADRD) has identified
several promising risk factors which could guide strategies to prevent up to 40% of AD/ADRD. Nearly all prior
evidence relies on observational data, which is prone to biases from unmeasured confounding, reverse
causation, selective survival, and measurement error. The Triangulation of Innovative Methods to End AD
(TIME-AD) program addresses these challenges by using an evidence triangulation framework for
strengthening causal inference in observational data. This framework systematically evaluates biases, planning
complementary analysis approaches with different data sources and study designs to rule out alternative
interpretations for the association of each risk factor and AD/ADRD: a) doubly robust observational methods
combining propensity score models with outcome models; b) instrumental variables (IV) methods using genetic
and policy variations that introduce random variation in exposure; and c) quantitative bias analysis to
characterize uncertainty. Project 1 will address the effects of alcohol use across the lifecourse on cognitive
aging and AD/ADRD risk. Project 2 will evaluate the effects of depression and depression treatment on
AD/ADRD risk and possible direct and modifying roles of chronic pain. Project 3 will assess whether
AD/ADRD risk may be reduced by prevention or treatment of vision or hearing impairments. Project 4 will
assess the impacts of social isolation, focusing on components of social isolation that are modifiable with
existing interventions. Each of these exposures is known to be associated with AD/ADRD outcomes in highly
educated, predominantly White populations; the current proposals will extend our knowledge by focusing on
causation, including large, diverse samples, and rigorously evaluating heterogeneity across populations. In
addition to an Administrative Core, projects will be supported by a Cognitive Outcomes, Exposure
Variables, and Covariates Data Core, which will help the intensive data management involved in constructing
analytic data sets and foster harmonized measures and coordinated analyses, drawing on multiple data
sources to support evidence triangulation. A Genetic and Policy Data Core will bring specialized expertise on
genetics, policies, and IV analysis, providing code to construct, validate, and implement IV analyses. An
Analytics Core will develop and share reusable analytic code and support implementation of the most up-to-
date methodology; and an Equity and Dissemination Core will ensure an equity focus is maintained
throughout TIME-AD, maximizing the relevance of our findings to improve outcomes among populations
typically underrepresented in AD/ADRD research. The Equity and Dissemination Core will ensure that
research implementation is guided by potential applications of the evidence and that the findings of each study
are broadly disseminated to stakeholders w...

## Key facts

- **NIH application ID:** 10934708
- **Project number:** 1P01AG082653-01A1
- **Recipient organization:** BOSTON UNIVERSITY MEDICAL CAMPUS
- **Principal Investigator:** Paola Gilsanz
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $5,262,301
- **Award type:** 1
- **Project period:** 2024-09-15 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10934708

## Citation

> US National Institutes of Health, RePORTER application 10934708, Improving causal inference in Alzheimer's Disease prevention research on modifiable risk factors: the Triangulation of Innovative Methods to EndAD (TIME-AD) project (1P01AG082653-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10934708. Licensed CC0.

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