Patterns of intra-cluster correlation from primary care research to inform study design and analysis
Abstract
Objective To provide information concerning the magnitude of the intraclass correlation coefficient (ICC) for cluster-based studies set in primary care.Study design and setting Reanalysis of data from 31 cluster-based studies in primary care to estimate intraclass correlation coefficients from random effects models using maximum likelihood estimation.Results ICCs were estimated for 1,039 variables. The median ICC was 0.010 (interquartile range [IQR] 0 to 0.032, range 0 to 0.840). After adjusting for individual- and cluster-level characteristics, the median ICC was 0.005 (IQR 0 to 0.021). A given measure showed widely varying ICC estimates in different datasets. In six datasets, the ICCs for SF-36 physical functioning scale ranged from 0.001 to 0.055 and for SF-36 general health from 0 to 0.072. In four datasets, the ICC for systolic blood pressure ranged from 0 to 0.052 and for diastolic blood pressure from 0 to 0.108.Conclusion The precise magnitude of between-cluster variation for a given measure can rarely be estimated in advance. Studies should be designed with reference to the overall distribution of ICCs and with attention to features that increase efficiency.
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With n=1200 (n=300 at each of four primary care practices) and an intracluster correlation (ICC) of 0.20 within practices, the study will have 0.80 power to detect a between-group difference of d=0.43.
“With n=1200 (n=300 at each of four primary care practices) and an intracluster correlation (ICC) of 0.20 within practices,46 we will have 0.80 power to detect a between-group difference of d=0.43 and 0.90 power to detect a between-group difference of d=0.49.”
With n=1200 and ICC 0.20, the study will have 0.90 power to detect a between-group difference of d=0.49.
“With n=1200 (n=300 at each of four primary care practices) and an intracluster correlation (ICC) of 0.20 within practices,46 we will have 0.80 power to detect a between-group difference of d=0.43 and 0.90 power to detect a between-group difference of d=0.49.”
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- Authors
- Adams, G., Gulliford, M. C., Ukoumunne, O. C., Eldridge, S., Chinn, S., & Campbell, M. J.
- Affiliations
- King's College London (GB)
- Queen Mary University of London (GB)
- University of Sheffield (GB)
- Corresponding author
- Gulliford, Martin C.
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2004-08
- Volume
- 57
- Issue
- 8
- Pages
- 785-794
- ISSN
- 0895-4356 (ISSN-L)
- OpenAlex
- W2085005241
- Cited by
- 602
- References
- 60
- Citations by year
- 2026: 25 · 2025: 35 · 2024: 33 · 2023: 29 · 2022: 56 · 2021: 48 · 2020: 47 · 2019: 27 · 2018: 32 · 2017: 43 · 2016: 21 · 2015: 36 · 2014: 41 · 2013: 16 · 2012: 27
- FWCI
- 2.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Clinical Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Research Portal (King's College London) (submittedVersion)
- Funders
- Medical Research Council
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Reliability and Agreement in Measurement
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- intraclass correlation coefficient, primary care, research design, sample size determination, random effects models, SF-36, blood pressure
- MeSH terms
- Data Interpretation, Statistical, Health Services Research, Humans, Primary Health Care, Research Design, Cluster Analysis, Randomized Controlled Trials as Topic
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