# Applying novel statistical approaches to develop a decision framework for hybrid randomized controlled trial designs which combine internal control arms with patients' data from real-world data source

> **NIH FDA U01** · GENENTECH, INC. · 2020 · $251,057

## Abstract

PROJECT ABSTRACT Technological advances in real world data (RWD) captured
from healthcare sources have enabled generation of an expanding body of real-world
evidence (RWE) on the use of medical products. These novel sources of evidence
can increase efficiencies of clinical trials by reducing sample size and/or shortening
trials duration, but have yet to be fully utilized. One application of RWD that could
significantly impact the conduct of clinical trials is the use of these data as external
controls. Of special interest
are hybrid randomized controlled trial designs, which
supplement internal control arms with patients’ level data from real-word data sources.
D
issimilarity between internal and external controls has the potential to negatively
impact the trial (e.g., decrease power, inflate type I error rate) as compared to
randomized control trials. Bayesian methods which adaptively adjust the influence of
external controls on the analysis of the trial data can help to mitigate these issues and
balance the risks and rewards associated with this type of complex trial designs.
Through our collaboration with the Department of Biostatistics at the University of
North Carolina (UNC) we are developing an adaptive borrowing approach with
subject-specific discounting parameters specifically suited for time-to-event analyses.
The proposed project would allow us to expand the UNC collaboration and develop a
novel decision framework (simulation tools, including R-Packages and where
computationally feasible SAS macros, and a set of study design considerations)
allowing reliable application of our method when using hybrid clinical trials for
regulatory decision making. We would focus on the following aims: (1) evaluation of
the hybrid designs and their operating characteristics, when combined with sequential
monitoring and possibly use of adaptive randomization, (2) assessment of possible
extensions of the method beyond time-to-event settings when applied to diseases in
different therapeutic areas, including rare diseases and (3) development of R-
Packages supporting study design simulations and offering training workshops on the
use of the packages to review staff at the FDA.
Where computationally feasible, we
will develop SAS macros as well and make these publicly available. To achieve our
aims, we will utilize data from completed clinical trials, RWD sources and simulation
studies. We plan to hold annual mini-conferences cross academia and industry to
explore how operating characteristics of the proposed designs could be utilized for
regulatory decision making and develop a recommended list of sensitivity analyses
that would support regulatory submissions based on hybrid study designs. Our
overarching objective is to make our developed decision framework publicly available.

## Key facts

- **NIH application ID:** 10186428
- **Project number:** 1U01FD007206-01
- **Recipient organization:** GENENTECH, INC.
- **Principal Investigator:** MICHAEL R KOSOROK
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** FDA
- **Fiscal year:** 2020
- **Award amount:** $251,057
- **Award type:** 1
- **Project period:** 2020-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10186428

## Citation

> US National Institutes of Health, RePORTER application 10186428, Applying novel statistical approaches to develop a decision framework for hybrid randomized controlled trial designs which combine internal control arms with patients' data from real-world data source (1U01FD007206-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10186428. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*