ArticleJournal of biopharmaceutical statistics2010
Exact inference for complex clustered data using within-cluster resampling.
Article in Journal of biopharmaceutical statistics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Development of a population-anchored Z-score for MRI-based knee osteoarthritis disease activity: Data from the Osteoarthritis Initiative.Osteoarthritis and cartilage open · 2026Article
- Validation of a Renal Papillary Grading System: Comparison of Patients Forming Calcium Oxalate and Apatite Stones.Journal of endourology · 2026Article
- A model for repeated clustered data with informative cluster sizes.Statistics in medicine · 2014Article
- Accounting for centre-effects in multicentre trials with a binary outcome - when, why, and how?BMC medical research methodology · 2014Article
- Assessing potential sources of clustering in individually randomised trials.BMC medical research methodology · 2013Article
- Weighted logrank tests for interval censored data when assessment times depend on treatment.Statistics in medicine · 2012Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
This paper introduces exact permutation methods for use when there are independent clusters of data with arbitrary within-cluster correlation. To eliminate the problem of clustering, we randomly select a data point from each cluster and for this now independent data, and calculate our test statistic and the associated support points for all possible permutations. While clearly valid, this is also inefficient. We repeat this process until all possible independent data sets have been created and use the support points averaged over the randomly created data sets as our reference distribution for the averaged test statistic. This approach uses all of the data and is a permutation extension of within-cluster resampling (WCR). We discuss both exact and Monte Carlo versions of the approach and apply it to several data sets. WCR permutation can be applied in quite general settings when within cluster correlation is a nuisance and exact inference is necessary.
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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.