Making Better Use of Randomized Trials: Assessing Applicability and Transporting Causal Effects [Methods Study], United States, 2015-2020 (ICPSR 39630)

Version Date: Dec 11, 2025

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Principal Investigator(s):
Issa J. Dahabreh, Harvard University

https://doi.org/10.3886/ICPSR39630.v1

Version V1

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Summary

Randomized controlled trials, or RCTs, look at how well treatments work. But people who take part in RCTs may differ from patients who receive care in clinics. For instance, patients who take part in RCTs may be less likely to smoke or may have fewer health problems. These differences can affect how well a treatment works. As a result, a treatment may work differently for a patient receiving care in a clinic than it did for patients who took part in the RCT.

Researchers can use statistical methods to account for differences in patient traits and behaviors. In this project, the research team developed and tested new methods to account for these differences. They used the methods to apply RCT results to patients receiving care in clinics.

To access the methods and software, please visit the generalizability_g_form_IPW and ExtendingInferences GitHub repositories.

Citation

Dahabreh, Issa J. Making Better Use of Randomized Trials: Assessing Applicability and Transporting Causal Effects [Methods Study], United States, 2015-2020. Inter-university Consortium for Political and Social Research [distributor], 2025-12-11. https://doi.org/10.3886/ICPSR39630.v1

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Funding

Patient-Centered Outcomes Research Institute (PCORI) (ME-1502-27794-IC)

Subject Terms

Geographic Coverage

Distributor(s)

Inter-university Consortium for Political and Social Research
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Time Period(s)

2015 -- 2020
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Study Purpose

To develop methods for extending RCT results to patients in clinical practice

Study Design

Researchers developed and evaluated methods for extending RCT results to patients seen in clinical practice.

Researchers combined baseline covariate, treatment, and outcome data from an RCT with baseline covariate data from a patient population. Based on the RCT's eligibility and participation criteria, researchers combined the data using two designs:

  • Nested trial designs in which the RCT data were embedded in a random sample of data from individuals from the patient population
  • Non-nested trial designs in which the RCT data were added to a random sample of data from individuals from the patient population

Under each study design, researchers specified identifiability conditions for extending inferences from RCTs to patients seen in clinical practice. Identifiability conditions are required conditions for determining average potential treatment outcomes in patients who did not participate in the RCT. For example, one condition is that the treatment effect found in the RCT stems from the randomized treatment and was not due to other factors such as trial participation itself.

Researchers then developed three types of statistical methods, called estimators, for estimating treatment outcomes and average treatment effects based on outcome modeling, modeling the probability of participation in the trial, and a combination of outcome modeling and modeling the probability of participation.

To evaluate the estimators' performance, researchers conducted studies using simulations and empirical data. Researchers used sensitivity analyses to assess how the methods worked when the identifiability conditions did not hold.Patients and healthcare providers provided input to the study team.

Universe

Consolidated database of 1,452 patient knees, half treated with a total knee replacement and the other half with non-surgical treatment using 4 databases.

Data Source

Data from an RCT, observational data from clinical practice, simulation data

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Original Release Date

2025-12-11

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Notes

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