ArticleBiostatistics (Oxford, England)2021
Randomization-based confidence intervals for cluster randomized trials.
Article in Biostatistics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Nurse Care Management of Opioid Use Disorder Treatment After 3 Years: A Secondary Analysis of the PROUD Cluster Randomized Clinical Trial.JAMA network open · 2024Trial
- Permutation tests for detecting treatment effect heterogeneity in cluster randomized trials.Statistical methods in medical research · 2025Article
- Nurse Care Management for Opioid Use Disorder Treatment: The PROUD Cluster Randomized Clinical Trial.JAMA internal medicine · 2023Article
- Permutation-based multiple testing corrections forStatistics in medicine · 2023Article
- A platform trial design for preventive vaccines against Marburg virus and other emerging infectious disease threats.Clinical trials (London, England) · 2022Article
- Design and analysis of cluster randomized trials with time-to-event outcomes under the additive hazards mixed model.Statistics in medicine · 2022Article
- Network methods and design of randomized trials: Application to investigation of COVID-19 vaccination boosters.Clinical trials (London, England) · 2022Article
- Constrained randomization and statistical inference for multi-arm parallel cluster randomized controlled trials.Statistics in medicine · 2022Article
- Stepped Wedge Cluster Randomized Trials: A Methodological Overview.World neurosurgery · 2022Review
- Randomization-based inference for a marginal treatment effect in stepped wedge cluster randomized trials.Statistics in medicine · 2021Article
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Authors and funding
2 authors.
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Abstract
In a cluster randomized trial (CRT), groups of people are randomly assigned to different interventions. Existing parametric and semiparametric methods for CRTs rely on distributional assumptions or a large number of clusters to maintain nominal confidence interval (CI) coverage. Randomization-based inference is an alternative approach that is distribution-free and does not require a large number of clusters to be valid. Although it is well-known that a CI can be obtained by inverting a randomization test, this requires testing a non-zero null hypothesis, which is challenging with non-continuous and survival outcomes. In this article, we propose a general method for randomization-based CIs using individual-level data from a CRT. This approach accommodates various outcome types, can account for design features such as matching or stratification, and employs a computationally efficient algorithm. We evaluate this method's performance through simulations and apply it to the Botswana Combination Prevention Project, a large HIV prevention trial with an interval-censored time-to-event outcome.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.