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Transplant data: sources, collection, and caveats
Author(s) -
Dickinson David M.,
Bryant Paula C.,
Williams M. Christian,
Levine Gregory N.,
Li Shiqian,
Welch James C.,
Keck Berkeley M.,
Webb Randall L.
Publication year - 2004
Publication title -
american journal of transplantation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.89
H-Index - 188
eISSN - 1600-6143
pISSN - 1600-6135
DOI - 10.1111/j.1600-6135.2004.00395.x
Subject(s) - medicine , scope (computer science) , data collection , data quality , quality (philosophy) , data science , intensive care medicine , computer science , operations management , statistics , mathematics , metric (unit) , philosophy , epistemology , economics , programming language
By examining the sources, quality and organization of transplant data available, as well as making observations about data reporting patterns and accuracy, we hope to improve understanding of existing results, help researchers with study design and stimulate new exploratory initiatives. The primary data source, collected by the OPTN, has benefited from extensive recent technological advances. Transplant professionals now report patient and donor data more easily, quickly, and accurately, improving data timeliness and precision. Secondary sources may be incorporated, improving the accuracy and expanding the scope of analyses. For example, auxiliary mortality data allows more accurate survival analysis and conclusions regarding the completeness of center‐reported post‐transplant follow‐up. Furthermore, such sources enable examination of outcomes not reported by centers, such as mortality after waiting list removal, providing more appropriate comparisons of waiting list and post‐transplant mortality. Complex collection and reporting processes require specific analytical methods and may lead to potential pitfalls. Patterns in the timing of reporting adverse events differ from those for ‘positive’ events, yielding the need for care in choosing cohorts and censor dates to avoid bias. These choices are further complicated by the use of multiple sources of data, with different time lags and reporting patterns.