Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriate investigated and resolved. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
A new initiative of the United States (U.S.) Dialysis Outcomes and Practice Patterns Study (DOPPS), the DOPPS Practice Monitor (DPM) provides up-to-date data and analyses to monitor trends in dialysis practice during implementation of the new Centers for Medicare and Medicaid Services (CMS) End-Stage Renal Disease (ESRD) Prospective Payment System (PPS; 2011–2014). We review DPM rationale, design, sampling approach, analytic methods, and facility sample characteristics. Using stratified random sampling, the sample of ~145 U.S. facilities provides results representative nationally and by facility type (dialysis organization size, rural/urban, free-standing/hospital-based), achieving coverage similar to the CMS sample frame at average values and tails of the distributions for key measures and patient characteristics. A publicly available Web report (www.dopps.org/DPM) provides detailed trends including demographic, comorbidity, and dialysis data, medications, vascular access, and quality of life. Findings are updated every 4 months and lagged only 3–4 months. Baseline data are from mid-2010, prior to the new PPS. In sum, the DPM provides timely, representative data to monitor the effects of the expanded PPS on dialysis practice. Findings can serve as an early warning system for possible adverse effects on clinical care and as a basis for community outreach, editorial comment, and informed advocacy.
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.
This paper considers the problem of record linkage between a household-level survey and an establishment-level frame in the absence of unique identifiers. Linkage between frames in this setting is challenging because the distribution of employment across establishments is highly skewed. To address these difficulties, this paper develops a probabilistic record linkage methodology that combines machine learning (ML) with multiple imputation (MI). This ML-MI methodology is applied to link survey respondents in the Health and Retirement Study to their workplaces in the Census Business Register. The linked data reveal new evidence that non-sampling errors in household survey data are correlated with respondents' workplace characteristics.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.