%0 Journal Article
%T Ongoing monitoring of data clustering in multicenter studies
%A Lauren B Guthrie
%A Emily Oken
%A Jonathan AC Sterne
%A Matthew W Gillman
%A Rita Patel
%A Konstantin Vilchuck
%A Natalia Bogdanovich
%A Michael S Kramer
%A Richard M Martin
%J BMC Medical Research Methodology
%D 2012
%I BioMed Central
%R 10.1186/1471-2288-12-29
%X In this article, we describe quality assurance efforts aimed at reducing the effect of measurement error in a recent follow-up of a large cluster-randomized controlled trial through periodic evaluation of intraclass correlation coefficients (ICCs) for continuous measurements. An ICC of 0 indicates the variance in the data is not due to variation between the centers, and thus the data are not clustered by center.Through our review of early data downloads, we identified several outcomes (including sitting height, waist circumference, and systolic blood pressure) with higher than expected ICC values. Further investigation revealed variations in the procedures used by pediatricians to measure these outcomes. We addressed these procedural inconsistencies through written clarification of the protocol and refresher training workshops with the pediatricians. Further data monitoring at subsequent downloads showed that these efforts had a beneficial effect on data quality (sitting height ICC decreased from 0.92 to 0.03, waist circumference from 0.10 to 0.07, and systolic blood pressure from 0.16 to 0.12).We describe a simple but formal mechanism for identifying ongoing problems during data collection. The calculation of the ICC can easily be programmed and the mechanism has wide applicability, not just to cluster randomized controlled trials but to any study with multiple centers or with multiple observers.Multicenter study designs have several advantages, including the opportunity for larger sample sizes and greater generalizability of findings than single-site studies. Along with these advantages, however, comes the possibility of inter-site variability as a result of procedural differences between the study centers, such as variations in applying the study protocol [1]. Systematic variation between centers will result in a high degree of clustering, whereby measurements on study subjects from the same site are more highly correlated with each other than with measurements f
%U http://www.biomedcentral.com/1471-2288/12/29