Verification bias on sensitivity and specificity measurements in diagnostic medicine: a comparison of some approaches used for correction


ÜNAL İ. , BURGUT H. R.

JOURNAL OF APPLIED STATISTICS, vol.41, no.5, pp.1091-1104, 2014 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 41 Issue: 5
  • Publication Date: 2014
  • Doi Number: 10.1080/02664763.2013.862217
  • Title of Journal : JOURNAL OF APPLIED STATISTICS
  • Page Numbers: pp.1091-1104

Abstract

Verification bias may occur when the test results of not all subjects are verified by using a gold standard. The correction for this bias can be made using different approaches depending on whether missing gold standard test results are random or not. Some of these approaches with binary test and gold standard results include the correction method by Begg and Greenes, lower and upper limits for diagnostic measurements by Zhou, logistic regression method, multiple imputation method, and neural networks. In this study, all these approaches are compared by employing a real and simulated data under different conditions.

Verification bias may occur when the test results of not all subjects are verified by using a gold standard. The correction for this bias can be made using different approaches depending on whether missing gold standard test results are random or not. Some of these approaches with binary test and gold standard results include the correction method by Begg and Greenes, lower and upper limits for diagnostic measurements by Zhou, logistic regression method, multiple imputation method, and neural networks. In this study, all these approaches are compared by employing a real and simulated data under different conditions.