Improving data quality in observational research studies: Report of the Cure Glomerulonephropathy (CureGN) network

• Type of Material: Article
• Authors: Brenda W. Gillespie, University of Michigan; Louis-Philippe Laurin, Hôpital Maisonneuve-Rosemont; Dawn Zinsser, Arbor Research Collaborative for Health; Richard Lafayette, Stanford University; Maddalena Marasa, Vagelos College of Physicians and Surgeons; Scott E. Wenderfer, Baylor College of Medicine; Suzanne Vento, NYU Grossman School of Medicine; Caroline Poulton, The University of North Carolina at Chapel Hill; Laura Barisoni, Duke University; Jarcy Zee, Arbor Research Collaborative for Health; Margaret Helmuth, Arbor Research Collaborative for Health; Francesca Lugani, Istituto Giannina Gaslini; Margret Kamel, Emory University; Peg Hill-Callahan, Arbor Research Collaborative for Health; Stephen M. Hewitt, National Cancer Institute (NCI); Laura H. Mariani, University of Michigan; William E. Smoyer, The Ohio State University; Larry Greenbaum, Emory University; Debbie S. Gipson, University of Michigan; Bruce M. Robinson, Arbor Research Collaborative for Health; Ali G. Gharavi, Vagelos College of Physicians and Surgeons; Lisa M. Guay-Woodford, Childrens National Health System; Howard Trachtman, NYU Grossman School of Medicine
• Language: English
• Date: 2021-06-01
• Publisher: Elsevier
• Publication Version: Final Published Version
• Copyright Statement: © 2021 Published by Elsevier Inc.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.conctc.2021.100749
• Title of Journal or Parent Work: Contemporary Clinical Trials Communications
• Volume: 22
• Start Page: 100749
• End Page: 100749
• Grant/Funding Information: This work was supported by the National Institutes of Health (NIH), National Institute of Diabetes and Digestive and Kidney Deseases (NIDDK, United States) [grant numbers: UM1DK100845, UM1DK100846, UM1DK100876, UM1DK100866, and UM1DK100867].; Dates of funding for CureGN-1 are 9/16/2013-5/31/2019, and for CureGN-2 are 8/16/2019-5/31/2024.; Laura Mariani is funded by the Integrative Molecular Epidemiology Approach to Identify Nephrotic Syndrome Subgroups project, funded by the NIDDK, United States [grant number K08 DK115891-01].; Patient recruitment was supported by NephCure Kidney International (United States).
• Keywords: Site performance; Quality metrics; Data quality; Observational studies; Kidney disease
• Research Categories: Health Sciences, Medicine and Surgery; Health Sciences, Pathology; Health Sciences, Oncology; Biology, Biostatistics

Abstract

Background High data quality is of crucial importance to the integrity of research projects. In the conduct of multi-center observational cohort studies with increasing types and quantities of data, maintaining data quality is challenging, with few published guidelines. Methods The Cure Glomerulonephropathy (CureGN) Network has established numerous quality control procedures to manage the 70 participating sites in the United States, Canada, and Europe. This effort is supported and guided by the activities of several committees, including Data Quality, Recruitment and Retention, and Central Review, that work in tandem with the Data Coordinating Center to monitor the study. We have implemented coordinator training and feedback channels, data queries of questionable or missing data, and developed performance metrics for recruitment, retention, visit completion, data entry, recording of patient-reported outcomes, collection, shipping and accessing of biological samples and pathology materials, and processing, cataloging and accessing genetic data and materials. Results We describe the development of data queries and site Report Cards, and their use in monitoring and encouraging excellence in site performance. We demonstrate improvements in data quality and completeness over 4 years after implementing these activities. We describe quality initiatives addressing specific challenges in collecting and cataloging whole slide images and other kidney pathology data, and novel methods of data quality assessment. Conclusions This paper reports the CureGN experience in optimizing data quality and underscores the importance of general and study-specific data quality initiatives to maintain excellence in the research measures of a multi-center observational study.