OBJECTIVES: Cluster randomised trials, in which groups of individuals are randomised, are increasingly being used in the health field. Adopting a clustered approach has implications for the design of such trials, and sample size calculations need to be inflated to accommodate for the clustering effect. Reliable estimates of intracluster correlation coefficients (ICCs) are required for robust sample size calculations to be made; however, little empirical evidence is available on their likely size, and on factors which influence their magnitude. The aim of this study was to generate empirical estimates of ICCs and to explore factors which may affect their magnitude. METHODS: Empirical estimates of ICCs were calculated for both process variables and patient outcomes from a number of datasets of primary and secondary care implementation studies. RESULTS: Estimates of ICCs varied according to setting and type of outcome. Estimates of ICCs for process variables were higher than those for patient outcomes, and estimates derived from secondary care were higher than those from primary care. ICCs for process variables in primary care were of the order of 0.05-0.15, whilst those in secondary care were of the order of 0.3. Estimates for patient outcomes in primary care were generally lower than 0.05. CONCLUSIONS: Adopting cluster randomisation has implications for the design, size and analysis of clinical trials. This study gives an insight into the potential size of ICCs in primary and secondary care, and provides a practical guide to researchers to aid the planning of future studies in this area.
OBJECTIVES: Cluster randomised trials, in which groups of individuals are randomised, are increasingly being used in the health field. Adopting a clustered approach has implications for the design of such trials, and sample size calculations need to be inflated to accommodate for the clustering effect. Reliable estimates of intracluster correlation coefficients (ICCs) are required for robust sample size calculations to be made; however, little empirical evidence is available on their likely size, and on factors which influence their magnitude. The aim of this study was to generate empirical estimates of ICCs and to explore factors which may affect their magnitude. METHODS: Empirical estimates of ICCs were calculated for both process variables and patient outcomes from a number of datasets of primary and secondary care implementation studies. RESULTS: Estimates of ICCs varied according to setting and type of outcome. Estimates of ICCs for process variables were higher than those for patient outcomes, and estimates derived from secondary care were higher than those from primary care. ICCs for process variables in primary care were of the order of 0.05-0.15, whilst those in secondary care were of the order of 0.3. Estimates for patient outcomes in primary care were generally lower than 0.05. CONCLUSIONS: Adopting cluster randomisation has implications for the design, size and analysis of clinical trials. This study gives an insight into the potential size of ICCs in primary and secondary care, and provides a practical guide to researchers to aid the planning of future studies in this area.
Authors: Guadalupe Ortega; Cristina Castellà; Carlos Martín-Cantera; Jose L Ballvé; Estela Díaz; Marc Saez; Juan Lozano; Lourdes Rofes; Concepció Morera; Antònia Barceló; Carmen Cabezas; Jose A Pascual; Raúl Pérez-Ortuño; Esteve Saltó; Araceli Valverde; Mireia Jané Journal: BMC Public Health Date: 2010-12-20 Impact factor: 3.295
Authors: Joanne E McKenzie; Denise A O'Connor; Matthew J Page; Duncan S Mortimer; Simon D French; Bruce F Walker; Jennifer L Keating; Jeremy M Grimshaw; Susan Michie; Jill J Francis; Sally E Green Journal: Implement Sci Date: 2010-11-10 Impact factor: 7.327
Authors: Michele Heisler; Timothy P Hofer; Mandi L Klamerus; Julie Schmittdiel; Joe Selby; Mary M Hogan; Hayden B Bosworth; Adam Tremblay; Eve A Kerr Journal: Trials Date: 2010-10-12 Impact factor: 2.279