Data Collection and Quality Control

  • Lawrence M. Friedman
  • Curt D. Furberg
  • David L. DeMets
  • David M. Reboussin
  • Christopher B. Granger

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.

Keywords

Data Entry Case Report Form Scientific Misconduct Data Entry Screen Electronic Data Entry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Lawrence M. Friedman
    • 1
  • Curt D. Furberg
    • 2
  • David L. DeMets
    • 3
  • David M. Reboussin
    • 4
  • Christopher B. Granger
    • 5
  1. 1.North BethesdaUSA
  2. 2.Division of Public Health SciencesWake Forest School of MedicineWinston-SalemUSA
  3. 3.Department Biostatistics and Medical InformaticsUniversity of WisconsinMadisonUSA
  4. 4.Department of BiostatisticsWake Forest School of MedicineWinston-SalemUSA
  5. 5.Department of MedicineDuke UniversityDurhamUSA

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