Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2288-12-29
Title: Ongoing monitoring of data clustering in multicenter studies
Authors: Guthrie, L.B
Oken, E
Sterne, J.A.C
Gillman, M.W
Patel, R
Vilchuck, K
Bogdanovich, N
Kramer, M.S 
Martin, R.M
Keywords: adult
analysis of variance
anthropometry
article
blood pressure
clinical competence
clinical practice
cluster analysis
education
female
health care quality
human
in service training
male
methodology
multicenter study (topic)
patient care
pediatrics
physician
psychological aspect
randomized controlled trial (topic)
reproducibility
standard
statistical analysis
statistics
treatment outcome
Adult
Analysis of Variance
Anthropometry
Blood Pressure
Clinical Competence
Cluster Analysis
Continuity of Patient Care
Data Interpretation, Statistical
Education
Female
Humans
Inservice Training
Male
Multicenter Studies as Topic
Outcome and Process Assessment (Health Care)
Pediatrics
Physician's Practice Patterns
Physicians
Quality Assurance, Health Care
Randomized Controlled Trials as Topic
Reproducibility of Results
Statistics as Topic
Treatment Outcome
Issue Date: 2012
Citation: Guthrie, L.B, Oken, E, Sterne, J.A.C, Gillman, M.W, Patel, R, Vilchuck, K, Bogdanovich, N, Kramer, M.S, Martin, R.M (2012). Ongoing monitoring of data clustering in multicenter studies. BMC Medical Research Methodology 12 : 29. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2288-12-29
Rights: Attribution 4.0 International
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. © 2012 Guthrie et al; licensee BioMed Central Ltd.
Source Title: BMC Medical Research Methodology
URI: https://scholarbank.nus.edu.sg/handle/10635/181611
ISSN: 14712288
DOI: 10.1186/1471-2288-12-29
Rights: Attribution 4.0 International
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