# Optimizing the Population Representativeness of Older Adults in Cancer Trials

> **NIH NIH R21** · UNIVERSITY OF FLORIDA · 2021 · $392,116

## 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 generalizability has long been a concern and widely documented, especially, in cancer
research community. Certain population subgroups, such as older adults, are often underrepresented in
cancer studies due to [[overly restrictive (and potentially unjustified) exclusion criteria,]] which are arguably the
biggest yet modifiable barriers causing low generalizability. Regulatory agencies (e.g., FDA), funding agencies
(e.g., NCI), and research communities (e.g., ASCO) have called and provided guidance to broaden trial
eligibility criteria to promote enrollment practices so that trials can better reflecting the population most likely to
use the drug if approved. Nevertheless, trial sponsors and investigators are reluctant to broaden eligibility
criteria due to concerns over potential increases in the risk of serious adverse events (SAEs) and their
negative impact on the investigational drug’s safety and effectiveness profile. As a consequence, in cancer
trials, elderlies are often excluded implicitly through excluding clinical characteristics that are more prevalent in
the elderly. There is a gap between the need to broaden trial criteria and ways available to [[identify
unjustified, overly restrictive exclusion criteria and then adjust them accordingly 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 cancer 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 cancer 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 will systematically analyze existing female breast, lung, and colorectal trials
in clinicaltrails.gov to create an ontology-driven, standardized library of CEC, validate GIST among cancer
trials, and develop [[statistical models on how adjustments to eligibility criteria, especially those that limit the
participation of older adults]], would affect (1) trial generalizability measured by GIST, and (2) outcomes (i.e.,
SAEs) of the ta...

## Key facts

- **NIH application ID:** 10180066
- **Project number:** 1R21CA253394-01A1
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Jiang Bian
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $392,116
- **Award type:** 1
- **Project period:** 2021-04-08 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10180066

## Citation

> US National Institutes of Health, RePORTER application 10180066, Optimizing the Population Representativeness of Older Adults in Cancer Trials (1R21CA253394-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10180066. Licensed CC0.

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