ArticleStatistics in medicine2023
Rank intraclass correlation for clustered data.
Article in Statistics in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Agreement and Reliability of the G-Force System: Force Plate and Load Cell for the Isometric Mid-Thigh Pull in Physically Active Adults: A Repeated-Measures Method-Comparison Study.Sensors (Basel, Switzerland) · 2026Article
- A pilot self-coach alignment audit in NCAA collegiate rowers: field feasibility, null findings, and design-sensitivity analysis for future self-verification research.Frontiers in sports and active living · 2026Article
- What normal used to be: Longitudinal adrenal, reproductive, and thyroid hormone profiles from four bowhead whales during a period of Arctic stability.Conservation physiology · 2026Article
- Article
- Accuracy and variability in clinical predictions of speech recognition outcomes for cochlear implant users.International journal of audiology · 2025Article
- Between- and Within-Cluster Spearman Rank Correlations.Statistics in medicine · 2025Article
- Visual Snow Syndrome is unstable: A longitudinal investigation of VSS symptoms in a Naïve population.Annals of clinical and translational neurology · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Clustered data are common in biomedical research. Observations in the same cluster are often more similar to each other than to observations from other clusters. The intraclass correlation coefficient (ICC), first introduced by R. A. Fisher, is frequently used to measure this degree of similarity. However, the ICC is sensitive to extreme values and skewed distributions, and depends on the scale of the data. It is also not applicable to ordered categorical data. We define the rank ICC as a natural extension of Fisher's ICC to the rank scale, and describe its corresponding population parameter. The rank ICC is simply interpreted as the rank correlation between a random pair of observations from the same cluster. We also extend the definition when the underlying distribution has more than two hierarchies. We describe estimation and inference procedures, show the asymptotic properties of our estimator, conduct simulations to evaluate its performance, and illustrate our method in three real data examples with skewed data, count data, and three-level ordered categorical data.
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Registered trials
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