# Innovative statistical methodologies to subgroup analysis in clinical trials

> **NIH FDA R13** · BRIGHAM AND WOMEN'S HOSPITAL · 2020 · $50,000

## Abstract

FDA PA 19‐306 Bierer, Barbara E., M.D.
 Innovative statistical methodologies to subgroup analysis in clinical trials
Project Summary
The Multi‐Regional Clinical Trial (MRCT) Center of Brigham and Women's Hospital and Harvard (MRCT
Center) is a research and policy center created to address the conduct, oversight, ethics and regulatory
environment of clinical trials, with a focus on multinational trials. To do the work, we function as a
independent convener to engage diverse stakeholders from industry, CROs, academia, patients and
patient advocacy groups, non‐profit organizations, and global regulatory agencies to address problems
in rigor and integrity of trials. In this proposal, we propose to convene a public conference of
statisticians, clinical trialists, regulators, and patient/patient advocates to discuss innovative statistical
methodologies to subgroup analysis in clinical trials. This conference is highly relevant to the FDA's
efforts to promote inclusion of individuals of diverse backgrounds and characteristics in clinical trials and
to considerations of regional differences in multi‐national trials. Different subgroups appear to
necessitate different approaches. That is, continuous (e.g. age), categorical (e.g. sex) , and overlapping
(e.g. co‐morbidity, polypharmacy) variables differ, and each may command different statistical analyses.
The conference will address traditional approaches, Bayesian methods, and other innovative models and
explore the advantages and limitations of each. Further, the role of visualization and graphical
representation will be discussed. Not all subgroup analyses must be performed at the level of the
individual clinical trial; analyses of post‐approval observational data may illuminate important
differences across subgroups that were not discoverable during product development. For the clinician
and for the patient, the important factor is not the average treatment effect, but rather whether the
benefit of the product or intervention will outweigh the risks for the individual likely to take the product.

## Key facts

- **NIH application ID:** 10038875
- **Project number:** 1R13FD006905-01
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Barbara E Bierer
- **Activity code:** R13 (R01, R21, SBIR, etc.)
- **Funding institute:** FDA
- **Fiscal year:** 2020
- **Award amount:** $50,000
- **Award type:** 1
- **Project period:** 2020-02-01 → 2021-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10038875

## Citation

> US National Institutes of Health, RePORTER application 10038875, Innovative statistical methodologies to subgroup analysis in clinical trials (1R13FD006905-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10038875. Licensed CC0.

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