New Causal Inference Methods for Cluster Randomized Trials with Post-Randomization Selection Bias [Methods Study], United States, 2019-2023 (ICPSR 39742)

Version Date: Mar 24, 2026

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Principal Investigator(s):
Fan Li, Duke University

https://doi.org/10.3886/ICPSR39742.v1

Version V1

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Summary

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.

Citation

Li, Fan. New Causal Inference Methods for Cluster Randomized Trials with Post-Randomization Selection Bias [Methods Study], United States, 2019-2023. Inter-university Consortium for Political and Social Research [distributor], 2026-03-24. https://doi.org/10.3886/ICPSR39742.v1

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Funding

Patient-Centered Outcomes Research Institute (PCORI) (ME-2019C1-16146)

Subject Terms

Geographic Coverage

Distributor(s)

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

2019 -- 2023
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Study Purpose

To develop and apply statistical methods and software for estimating treatment effects in CRTs in the presence of post-randomization confounding

Study Design

The research team developed principal stratification methods to estimate causal effects for different patient groups created based on intermediate factors. The separate effects represent HTE in the population. The methods addressed three types of post-randomization confounding:

  • Identification bias. The intervention affects the identification of eligible patients, such as when diagnosis rates increase at clinics assigned to one treatment, resulting in more eligible patients.
  • Recruitment bias. The intervention affects recruitment, such as patients selecting clinics because they offer a specific treatment.
  • Noncompliance bias. Patients decide to stop their assigned treatment or switch to a clinic assigned to a different treatment.

First, the research team developed methods for estimating treatment effectiveness presence of identification or recruitment bias. The team applied these methods to the data from the Primary Care Opioid Use Disorders Treatment (PROUD) trial, which examined acute care utilization among patients diagnosed with opioid use disorder.

Next, the research team developed one Bayesian method and one weighting method to address noncompliance bias in estimating time-to-event outcomes in CRTs. The team applied existing and new methods to data from Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness (ADAPTABLE), which compared risk for a major adverse cardiovascular event with varying aspirin doses. The research team developed an R software package, PStrata, to support principal stratification analysis for CRTs.

A physician, a cardiologist, and a statistician provided input during the study.

Data Source

Data from two CRTs:The PROUD trial tested the effectiveness of the PROUD intervention, which used medication to treat opioid use disorder within primary care settings to reduce acute care utilization ADAPTABLE compared risk of a major cardiovascular event with a high versus a low dose of aspirin taken preventively

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Original Release Date

2026-03-24

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Notes

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