Quality Control and Good Epidemiological Practice

  • Preetha Rajaraman
  • Jonathan M. Samet


The use of data is fundamental in epidemiology. Epidemiologic research on causation uses data in a search for the true nature of the relationship between exposure and disease. Similarly, research on the consequences of interventions seeks an unbiased characterization of the effects of independently varying factors on the outcome measure(s). One of the most rewarding moments for a researcher is obtaining the preliminary results from his or her study. However, the question “do I believe what I see?” should immediately come to mind. The answer to this question is determined in large part by the more mundane but critical question of how good is the quality of the data, rather than by the elegance of the scientific method. Errors that occur during study population selection or in the measurement of study exposures, outcomes, or covariates can lead to a biased estimate of the effect of exposure on risk for the disease of interest. Misclassification of exposure or disease that occurs randomly between all study participants decreases the power of the study to detect an association where it exists. Data collection that is differentially biased may have more severe consequences, and can lead to an incorrect assessment of the relationship between exposure and disease.


Quality Assurance Data Entry Monetary Incentive Study Personnel Multiple Risk Factor Intervention Trial 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Preetha Rajaraman
    • 1
  • Jonathan M. Samet
    • 2
  1. 1.Division of Cancer Epidemiology and GeneticsNational Cancer InstituteMarylandUSA
  2. 2.Department of Epidemiology Bloomberg School of Public HealthThe Johns Hopkins UniversityBaltimoreUSA

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