Patterns of intra-cluster correlation from primary care research to inform study design and analysis
To provide information concerning the magnitude of the intraclass correlation coefficient (ICC) for cluster-based studies set in primary care. Reanalysis of data from 31 cluster-based studies in primary care to estimate intraclass correlation coefficients from random effects models using maximum lik...
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| Veröffentlicht in: | Journal of clinical epidemiology 2004-08, Vol.57 (8), p.785-794 |
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| container_title | Journal of clinical epidemiology |
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| creator | Adams, Geoffrey Gulliford, Martin C. Ukoumunne, Obioha C. Eldridge, Sandra Chinn, Susan Campbell, Michael J. |
| description | To provide information concerning the magnitude of the intraclass correlation coefficient (ICC) for cluster-based studies set in primary care.
Reanalysis of data from 31 cluster-based studies in primary care to estimate intraclass correlation coefficients from random effects models using maximum likelihood estimation.
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.
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. |
| doi_str_mv | 10.1016/j.jclinepi.2003.12.013 |
| format | Article |
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Reanalysis of data from 31 cluster-based studies in primary care to estimate intraclass correlation coefficients from random effects models using maximum likelihood estimation.
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.
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.</description><identifier>ISSN: 0895-4356</identifier><identifier>EISSN: 1878-5921</identifier><identifier>DOI: 10.1016/j.jclinepi.2003.12.013</identifier><identifier>PMID: 15485730</identifier><language>eng</language><publisher>New York, NY: Elsevier Inc</publisher><subject>Adjustment ; Biological and medical sciences ; Blood pressure ; Cardiovascular disease ; Cluster Analysis ; Cluster randomization ; Cluster sampling ; Correlation coefficient ; Data Interpretation, Statistical ; Design effect ; Epidemiology ; Estimates ; General aspects ; General practice ; Health Services Research - methods ; Hospitals ; Humans ; Intraclass correlation ; Medical sciences ; Methodology ; Primary care ; Primary Health Care - methods ; Public health. Hygiene ; Public health. Hygiene-occupational medicine ; Randomized Controlled Trials as Topic - methods ; Research Design ; Sample size ; Studies ; Variables</subject><ispartof>Journal of clinical epidemiology, 2004-08, Vol.57 (8), p.785-794</ispartof><rights>2004 Elsevier Inc.</rights><rights>2005 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c488t-f804e830be2f8a32ea53dd7142b620345a9aa80eafd9bf5aa98d7ac1862c57e63</citedby><cites>FETCH-LOGICAL-c488t-f804e830be2f8a32ea53dd7142b620345a9aa80eafd9bf5aa98d7ac1862c57e63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/1033173802?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,3549,27923,27924,45994,64384,64386,64388,72340</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=16476916$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/15485730$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Adams, Geoffrey</creatorcontrib><creatorcontrib>Gulliford, Martin C.</creatorcontrib><creatorcontrib>Ukoumunne, Obioha C.</creatorcontrib><creatorcontrib>Eldridge, Sandra</creatorcontrib><creatorcontrib>Chinn, Susan</creatorcontrib><creatorcontrib>Campbell, Michael J.</creatorcontrib><title>Patterns of intra-cluster correlation from primary care research to inform study design and analysis</title><title>Journal of clinical epidemiology</title><addtitle>J Clin Epidemiol</addtitle><description>To provide information concerning the magnitude of the intraclass correlation coefficient (ICC) for cluster-based studies set in primary care.
Reanalysis of data from 31 cluster-based studies in primary care to estimate intraclass correlation coefficients from random effects models using maximum likelihood estimation.
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.
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.</description><subject>Adjustment</subject><subject>Biological and medical sciences</subject><subject>Blood pressure</subject><subject>Cardiovascular disease</subject><subject>Cluster Analysis</subject><subject>Cluster randomization</subject><subject>Cluster sampling</subject><subject>Correlation coefficient</subject><subject>Data Interpretation, Statistical</subject><subject>Design effect</subject><subject>Epidemiology</subject><subject>Estimates</subject><subject>General aspects</subject><subject>General practice</subject><subject>Health Services Research - methods</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Intraclass correlation</subject><subject>Medical sciences</subject><subject>Methodology</subject><subject>Primary care</subject><subject>Primary Health Care - methods</subject><subject>Public health. Hygiene</subject><subject>Public health. 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Reanalysis of data from 31 cluster-based studies in primary care to estimate intraclass correlation coefficients from random effects models using maximum likelihood estimation.
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.
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.</abstract><cop>New York, NY</cop><pub>Elsevier Inc</pub><pmid>15485730</pmid><doi>10.1016/j.jclinepi.2003.12.013</doi><tpages>10</tpages></addata></record> |
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| ispartof | Journal of clinical epidemiology, 2004-08, Vol.57 (8), p.785-794 |
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| subjects | Adjustment Biological and medical sciences Blood pressure Cardiovascular disease Cluster Analysis Cluster randomization Cluster sampling Correlation coefficient Data Interpretation, Statistical Design effect Epidemiology Estimates General aspects General practice Health Services Research - methods Hospitals Humans Intraclass correlation Medical sciences Methodology Primary care Primary Health Care - methods Public health. Hygiene Public health. Hygiene-occupational medicine Randomized Controlled Trials as Topic - methods Research Design Sample size Studies Variables |
| title | Patterns of intra-cluster correlation from primary care research to inform study design and analysis |
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