# Optimizing the Population Representativeness of Older Adults in Alzheimer's Disease and Related Dementia Clinical Trials

> **NIH NIH R21** · UNIVERSITY OF FLORIDA · 2020 · $228,750

## Abstract

ABSTRACT
Clinical trials are often conducted under idealized and rigorously controlled conditions to ensure internal
validity, but such conditions, paradoxically, compromise trials external validity (i.e., generalizability to the target
population). Low trial generalizability has long been a concern and widely documented across different clinical
areas. For instance, participants of Alzheimer's disease and related dementias (ADRD) clinical trials are
systematically younger than ADRD patients in the general population. Overly restrictive eligibility criteria are
arguably the biggest yet modifiable barriers causing low generalizability. The FDA has launched numerous
initiatives, primarily through broadening eligibility criteria, to promote enrollment practices so that trial
participants can better reflect the population who would most likely use the treatment if approved.
Nevertheless, trial sponsors and investigators are reluctant to broaden eligibility criteria due to concerns over
potential increases in risk of serious adverse events (SAEs) and its negative impact on the investigational
drug’s safety and effectiveness profile. As a result, many elderly patients are excluded from ADRD trials either
explicitly through an age restriction or implicitly through excluding clinical characteristics more prevalent in the
elderly. There is a gap between the need to broaden trial criteria and ways available to fulfill the need in
practice. Previous studies, including ours, have validated and used the Generalizability Index of Study Traits
(GIST), the best available quantitative, eligibility-driven, a priori generalizability measure, in a number of
disease domains. GIST scores can potentially be used to guide adjustments to criteria towards better
population representativeness. However, there are key barriers for its adoption in practice, especially in ADRD
trials: (1) the lack of a standardized, computable eligibility criteria (CEC) framework to translate criteria to data
queries – a necessary step to define the populations for generalizability assessment, (2) the lack of a validation
study that assesses GIST’s reliability and validity in ADRD trials, and (3) the need to map the mathematical
relationships between eligibility criteria and GIST as well as patient outcomes (i.e. SAE), which answers the
critical question how broadened criteria will affect trial’s generalizability and patient outcomes simultaneously.
To remove these barriers, we propose to systematically analyze existing ADRD trials in clinicaltrails.gov to
create an ontology-driven, standardized library of CEC for ADRD trials, validate GIST among ADRD trials, and
develop statistical models on how adjustments to eligibility criteria, especially age, would affect (1) trial
generalizability measured by GIST, and (2) outcomes (i.e., SAEs) of the target population, approximated using
real-world electronic health record (EHR) data. We will answer a key research question: what and how
exclusion crite...

## Key facts

- **NIH application ID:** 10041303
- **Project number:** 1R21AG068717-01
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Jiang Bian
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $228,750
- **Award type:** 1
- **Project period:** 2020-08-01 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10041303

## Citation

> US National Institutes of Health, RePORTER application 10041303, Optimizing the Population Representativeness of Older Adults in Alzheimer's Disease and Related Dementia Clinical Trials (1R21AG068717-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10041303. Licensed CC0.

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