Intraclass correlation coefficients for cluster randomized trials in primary care: The cholesterol education and research trial (CEART)

• Published in: Contemporary clinical trials Vol. 26; no. 2; pp. 260 - 267
• Main Authors: Parker, Donna R., Evangelou, Evangelos, Eaton, Charles B.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 01.04.2005; Elsevier
• Subjects: Adult; Aged; Aged, 80 and over; Biological and medical sciences; Blood Pressure - physiology; Cholesterol - blood; Cluster Analysis; Cluster randomized trials; Computers, Handheld; Decision Support Systems, Clinical; Female; Humans; Intraclass correlation coefficients; Male; Mass Screening; Medical sciences; Middle Aged; Patient Education as Topic; Pharmacology. Drug treatments; Practice Guidelines as Topic; Primary care; Primary Health Care - methods; Primary Health Care - standards; Research Design; Sample Size; Primary care; Cluster randomized trials; Intraclass correlation coefficients; Correlation coefficient; Randomization; Hypercholesterolemia; Metabolic diseases; Lipids; Hyperlipoproteinemia; Lipoprotein; Dyslipemia; Cholesterol
• ISSN: 1551-7144; 1559-2030
• DOI: 10.1016/j.cct.2005.01.002

Cluster randomization trials are increasingly being used in primary care research. The main feature of these trials is that patients are nested within large clusters such as physician practices or communities and the intervention is applied to the cluster. This study design necessitates calculation of intraclass correlation coefficients in order to determine the required sample size. The purpose of this study is to determine intraclass correlation coefficients for a number of outcome measures at the primary care practice level. The CEART study is a randomized trial testing the effectiveness of translating ATP III guidelines into clinical practice, with primary care physician practices as the unit of randomization and patients as the unit of data collection. The intraclass correlation coefficient (ICC) was<0.02 and the design effect ranged from 1.0 to 2.3, respectively, for weight, total cholesterol, LDL, non-HDL, glucose, creatinine, and % at non-HDL goal. For smoking status, body mass index, systolic blood pressure, HDL cholesterol triglycerides, total cholesterol/HDL ratio and % at LDL goal, the ICC was 0.02–0.047 and the design effect was 2.6–4.1. The largest ICCs (0.05–0.12) and design effects (4.4–9.4) were found for height and diastolic blood pressure. These findings suggest that cluster randomization may substantially increase the sample size necessary to maintain adequate statistical power for selected outcomes such as diastolic blood pressure studies compared with simple randomization for most outcomes evaluated in this study where the design effect is small to moderate. Overall, the ICCs presented will be useful in calculating sample sizes at the primary care level.