Results Summary

What was the project about?

Cluster randomized trials, or CRTs, are studies that compare treatments across different groups of patients, or clusters. An example of a cluster is people who receive care at one clinic.

To reduce bias in CRT results, researchers assign clusters by chance to different treatments. But what happens after they assign treatment can lead to differences across clusters and bias the results. For example, patients who visit clinics assigned to a treatment may be older than patients who visit clinics not assigned to that treatment. Current statistical methods for analyzing data from CRTs don’t work well to account for these differences.

In this study, the research team developed new methods to account for differences across clusters after treatment assignment.

What did the research team do?

The research team created new methods to address three types of bias that can lead to differences across clusters in CRTs after treatment assignment:

  • Identification bias. This type of bias happens when the treatment affects which patients are eligible to take part in a trial. For example, if more patients receive a diagnosis at the clinic after treatment assignment, then more patients at that clinic would be eligible to take part in the trial.
  • Recruitment bias. This type of bias occurs when the treatment affects who enrolls in the trial. For example, patients may choose to go to a clinic because it offers a certain treatment.
  • Noncompliance bias. This type of bias happens when patients don’t stay on their assigned treatment during the trial. They may stop treatment or switch to a clinic assigned to a different treatment.

The research team developed new methods to estimate how well treatments work when identification or recruitment bias are present. The team applied the new methods to data from a completed CRT that looked at healthcare use among patients with opioid use disorder.

Then the research team developed two new methods to estimate how well treatments work when noncompliance bias is present. They applied current and new methods to data from a completed CRT on heart disease. The CRT compared the effect of taking low or high doses of aspirin on risk of heart-related health outcomes. The team compared results from current and new methods.

Two doctors and a statistician provided input during the study.

What were the results?

The new methods accounted for the three types of bias. In both completed CRTs, the new methods were more accurate in measuring how well treatments worked than existing methods.

The research team developed a software package for applying the new methods to data from CRTs.

What were the limits of the project?

The new methods worked with CRT data collected at one point in time. The methods may not work with data from CRTs collected over time.

Future research could test the methods with data collected over time.

How can people use the results?

Researchers can use the new methods to account for differences across clusters after treatment assignment in CRTs.

Final Research Report

This project's final research report is expected to be available by October 2024.

Peer-Review Summary

The Peer-Review Summary for this project will be posted here soon.

Conflict of Interest Disclosures

Project Information

Fan Li, PhD
Duke University
$941,874
New Causal Inference Methods for Cluster Randomized Trials with Post-Randomization Selection-Bias

Key Dates

November 2019
January 2024
2019
2024

Study Registration Information

Tags

Has Results
Award Type
State State The state where the project originates, or where the primary institution or organization is located. View Glossary
Last updated: January 25, 2024