# A sensitivity analysis framework for generalizing randomized clinical trial results in the presence of unmeasured treatment effect modifiers

> **NIH NIH R03** · OHIO STATE UNIVERSITY · 2024 · $90,722

## Abstract

ABSTRACT
Randomized controlled trials (RCTs) are the gold standard for assessing interventions for preventing and
treating cancer, but their external validity is only guaranteed if the trial participants are a random sample from
the target population. Unfortunately, most cancer-related RCTs use convenience samples, not probability
samples, and differences between the trial sample and the target population are likely to exist. If these
differences are related to the effectiveness of the treatment being studied (“effect modifiers”), trial results will
fail to generalize. While observable differences may be assessed and potentially adjusted for (e.g.,
underrepresentation of certain demographic groups), these differences have been shown to not completely
explain the so-called efficacy-effectiveness gap. We posit that unmeasured differences between who chooses
to participate in an RCT and who does not may be an important contributor to the failure of some trial results to
generalize. In this project, we propose to develop a statistical framework for quantifying the potential impact of
unmeasured differences between the trial sample and the target population on trial results. The resulting
sensitivity analysis will bound the potential bias in the treatment effect estimate when generalizing from the trial
sample to a target population. The methodology will be based on our prior work developing sensitivity analyses
in the areas of survey nonresponse and selection bias which similarly consider the issue of differences
between who is in a study sample and who is not. This work will have broad applicability beyond cancer trials,
as generalizability is a universal concern of randomized trials across application areas.

## Key facts

- **NIH application ID:** 10788836
- **Project number:** 1R03CA280007-01A1
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** Rebecca Roberts Andridge
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $90,722
- **Award type:** 1
- **Project period:** 2024-01-01 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10788836

## Citation

> US National Institutes of Health, RePORTER application 10788836, A sensitivity analysis framework for generalizing randomized clinical trial results in the presence of unmeasured treatment effect modifiers (1R03CA280007-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10788836. Licensed CC0.

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