Data Collection and Quality Control

Lawrence M. Friedman, Curt D. Furberg, David L. DeMets, David M. Reboussin and Christopher B. Granger
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Curt D. Furberg: Wake Forest School of Medicine, Division of Public Health Sciences
David L. DeMets: University of Wisconsin, Department Biostatistics and Medical Informatics
David M. Reboussin: Wake Forest School of Medicine, Department of Biostatistics
Christopher B. Granger: Duke University, Department of Medicine

Chapter Chapter 11 in Fundamentals of Clinical Trials, 2015, pp 233-253 from Springer

Abstract: Abstract Valid and informative results from clinical trials depend on data that are of high enough quality and sufficiently robust to address the question posed. Such data in clinical trials are collected from several sources—medical records (electronic and paper), interviews, questionnaires, participant examinations, laboratory determinations, or public sources like national death registries. Data elements vary in their importance, but having valid data regarding key descriptors of the population, the intervention, and primary outcome measures is essential to the success of a trial. Equally important, and sometimes a trade-off given limited resources, is having a large enough sample size and number of outcome events to obtain a sufficiently narrow estimate of the intervention effect. Modest amounts of random errors in data will not usually affect the interpretability of the results, as long as there are sufficient numbers of outcome events. However, systematic errors can invalidate a trial’s results.

Date: 2015
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DOI: 10.1007/978-3-319-18539-2_11

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