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

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.

Keywords:

• verification bias
• Begg and Greenes correction
• multiple imputation
• MAR
• NMAR

Acknowledgements

This research was supported by the Scientific Research Project Unit of Cukurova University, Grant No. TF2007D10.