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BMC Med Res Methodol. 2012; 12: 29.
Published online Mar 13, 2012. doi:  10.1186/1471-2288-12-29
PMCID: PMC3313871
Ongoing monitoring of data clustering in multicenter studies
Lauren B Guthrie,1 Emily Oken,corresponding author1,6 Jonathan AC Sterne,2 Matthew W Gillman,1 Rita Patel,2 Konstantin Vilchuck,5 Natalia Bogdanovich,5 Michael S Kramer,3,4 and Richard M Martin2
1Obesity Prevention Program, Department of Population Medicine, Harvard Medical School and the Harvard Pilgrim Health Care Institute, Boston, USA
2School of Social and Community Medicine, University of Bristol, Bristol, UK
3Department of Pediatrics, McGill University Faculty of Medicine, Montreal, Canada
4Department of Epidemiology and Biostatistics, McGill University Faculty of Medicine, Montreal, Canada
5The National Research and Applied Medicine Mother and Child Center, Minsk, Republic of Belarus
6Obesity Prevention Program, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 133 Brookline Avenue, 6th Floor, Boston, MA 02215, USA
corresponding authorCorresponding author.
Lauren B Guthrie: emily_oken/at/harvardpilgrim.org; Emily Oken: emily_oken/at/harvardpilgrim.org; Jonathan AC Sterne: jonathan.sterne/at/bristol.ac.uk; Matthew W Gillman: matthew_gillman/at/harvardpilgrim.org; Rita Patel: rita.patel/at/bristol.ac.uk; Konstantin Vilchuck: sevenhos/at/mail.belpak.by; Natalia Bogdanovich: magicienne/at/tut.by; Michael S Kramer: michael.kramer/at/mcgill.ca; Richard M Martin: richard.martin/at/bristol.ac.uk
Received July 18, 2011; Accepted March 13, 2012.
Abstract
Background
Multicenter study designs have several advantages, but the possibility of non-random measurement error resulting from procedural differences between the centers is a special concern. While it is possible to address and correct for some measurement error through statistical analysis, proactive data monitoring is essential to ensure high-quality data collection.
Methods
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.
Results
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).
Conclusions
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.
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