Intra-cluster correlation coefficients in adults with diabetes in primary care practices: the Vermont Diabetes Information System field survey

Littenberg, Benjamin; MacLean, Charles D

doi:10.1186/1471-2288-6-20

Cited by 74Statistical Methods in EpidemiologyStatistical Methods in Clinical TrialsSurvey Sampling and Estimation Techniques

Intra-cluster correlation coefficients in adults with diabetes in primary care practices: the Vermont Diabetes Information System field survey

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Littenberg, Benjamin, MacLean, Charles D·2006·BMC Medical Research Methodology

DOI 10.1186/1471-2288-6-20PMC PMC1513389

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Abstract

Background: Proper estimation of sample size requirements for cluster-based studies requires estimates of the intra-cluster correlation coefficient (ICC) for the variables of interest. Methods: We calculated the ICC for 112 variables measured as part of the Vermont Diabetes Information System, a cluster-randomized study of adults with diabetes from 73 primary care practices (the clusters) in Vermont and surrounding areas. Results: ICCs varied widely around a median value of 0.0185 (Inter-quartile range: 0.006, 0.037). Some characteristics (such as the proportion having a recent creatinine measurement) were highly associated with the practice (ICC = 0.288), while others (prevalence of some comorbidities and complications and certain aspects of quality of life) varied much more across patients with only small correlation within practices (ICC<0.001). Conclusion: The ICC values reported here may be useful in designing future studies that use clustered sampling from primary care practices.

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The study was designed to have 90% power to detect a difference of 0.5% in A1C levels, using a two-sample t test (two-sided, α = 0.05), assuming a common SD of 1.5%; 408 patients were required.
“The study was designed to have a 90% power to detect a difference of 0.5% in A1C levels. This was determined using a two-sample t test (two-sided, α = 0.05), assuming a common SD of 1.5%. The estimate of SD in A1C values was inflated from 1.15 to 1.50 because of the clustering effect (18,19). We required a total of 408 patients (204 per study arm) to achieve the specified statistical power.”
From Structured Self-Monitoring of Blood Glucose Significantly Reduces A1C Levels in Poorly Controlled, Noninsulin-Treated Type 2 Diabetes (2011) · cites this paper as [18]
The Background explains that clustered designs require an inflation adjustment when estimating sample size, which supports the rationale for accounting for clustering. It does not report an A1C SD adjustment from 1.15 to 1.50; the citing paper’s power target and 408-patient requirement are not the part attached to this citation. No page number is available in our copy. (LLM, conf=0.86)
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Authors: Littenberg, B., & MacLean, C. D.
Affiliations: University of Vermont (US)
Corresponding author: Littenberg, Benjamin
ORCID: Littenberg, Benjamin
MacLean, Charles D
Venue: BMC Medical Research Methodology
Publisher: Springer Science and Business Media LLC
Published: 2006-05-03
Volume: 6
Issue: 1
Pages: 20-20
ISSN: 1471-2288 (ISSN-L)
DOI: 10.1186/1471-2288-6-20
PMC: PMC1513389
OpenAlex: W1815444468
Cited by: 73
References: 28
Citations by year: 2025: 2 · 2024: 3 · 2023: 1 · 2022: 2 · 2021: 4 · 2020: 7 · 2019: 9 · 2018: 5 · 2017: 5 · 2016: 2 · 2015: 8 · 2014: 4 · 2013: 7 · 2012: 4
FWCI: 1.26
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
www.springer.com/tdm (unspecified)
Locations: BMC Medical Research Methodology (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders: National Institute of Diabetes and Digestive and Kidney Diseases (R01DK61167, K24DK068380, R01DK061167)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
Keywords: intra-cluster correlation, diabetes mellitus, primary care practices, sample size determination, cluster sampling, comorbidities, quality of life, creatinine measurement, clustered data
MeSH terms: Adult, Data Interpretation, Statistical, Diabetes Mellitus, Female, Health Services Research, Humans, Information Systems, Male, Primary Health Care, Surveys and Questionnaires, Vermont, Cluster Analysis, Biomedical Research

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Intra-cluster correlation coefficients in adults with diabetes in primary care practices: the Vermont Diabetes Information System field survey

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