# Transporting effects using a multicenter randomized study to different target populations

> **NIH AHRQ R36** · BROWN UNIVERSITY · 2021 · $43,083

## Abstract

PROJECT SUMMARY
 Large confirmatory or pragmatic clinical trials enroll patients from multiple centers to obtain high-quality
evidence useful for decision-making in diverse healthcare systems. The design of multicenter trials is
particularly appealing to policymakers because many tend to be pragmatic and offer access to a large sample
of diverse patient populations. Decision-makers trying to use the results of multicenter trials have specific
target populations in mind. Yet, selection at both the center and individual participant level challenges the
ability of even well-designed and conducted multicenter trials to draw inferences about any meaningful target
population. These selection mechanisms, even in impeccably conducted multicenter trials, result in samples of
trial participants that are not representative of any specific target population. When treatment effects are
heterogeneous over covariates that are differentially distributed across centers, the overall trial average
treatment effect estimate will not apply to any one of the populations underlying the participating centers or the
target population. Consider for example, the National Lung Screening Trial (NLST), a large multicenter trial of
33 centers (53,456 individuals) across the United States, which motivated this proposal. Because the positive
benefits of screening are mainly concentrated in individuals at high-risk for lung cancer it is critical that
individuals are appropriately targeted for screening recommendations. Our long-term objective is to optimize
the applicability of trial results into clinical practice. We will take steps toward this objective by achieving the
following specific aims: (1) Develop robust and efficient statistical methods, that can be combined with modern
machine learning techniques, to reinterpret a multicenter trial in the context of each of the participating centers
and to transport inferences from a multicenter trial to a new target population; (2) Evaluate the performance of
methods developed in Aim 1 in simulation studies that reflect real-world data by considering scenarios with
different sample sizes, number of centers, treatment effects, and outcome or selection mechanisms; (3) Apply
the methods in the NLST to reinterpret the trial in the context of one of the participating centers and to
transport trial findings to a new target population using baseline data from the National Health and Nutrition
Examination Survey, a nationally representative survey of the United States general population. The proposed
work is innovative for analyzing multicenter trials because it develops new methods, clarifies the assumptions
needed for obtaining valid inference, and empirically assesses method performance. This work is significant
because as a result of applying these methods, policymakers will be able to learn about treatment effects in
different underlying populations and produce high-quality evidence of interventions in diverse populations that
are ofte...

## Key facts

- **NIH application ID:** 10228266
- **Project number:** 1R36HS028373-01
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** Sarah E Robertson
- **Activity code:** R36 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2021
- **Award amount:** $43,083
- **Award type:** 1
- **Project period:** 2021-04-01 → 2022-01-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10228266

## Citation

> US National Institutes of Health, RePORTER application 10228266, Transporting effects using a multicenter randomized study to different target populations (1R36HS028373-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10228266. Licensed CC0.

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