Verification Bias Correction in Endometrial Abnormalities in Infertile Women Referred to Royan Institute Using Statistical Methods

Abstract

Background: Verification bias is a common bias in the diagnostic accuracy of diagnostic tests and occurs when a number of individuals do not perform the gold standard test. In this study, we review the correcting methods of verification bias. Methods: In a cross-sectional study in 2020, 567 infertile women who were referred to Royan Research Institute were evaluated. The ultrasound is the performed test and the gold standard are hysteroscopy for some, and pathology for other abnormalities. For correcting verification bias conventional, Begg and Greens, Zhou, and logistic regression methods were used. Results: In the gold standard hysteroscopy test, the sensitivity (SEN) and specificity (SPEC) obtained in conventional, Begg and Greens, Zhou, and logistics Regression methods were (50%, 90.3%), (48%, 96%), (22%, 77%), (50%, 90%), and (72.8, 77) respectively. Furthermore, the area under the curve (AUC) index and kappa statistics were calculated as 70.2%, and 43.6% respectively. In the pathology gold standard test, the SEN and SPEC for the conventional methods, Begg and Greens, Zhou and logistics regression were (67.7%, 86.7%), (66%, 88%), (29%, 70%), (66.9%, 87.6%), and (73%, 83.9%) respectively. Also, the AUC index and kappa statistics were 77%, and 55% respectively. Conclusion: In the study on endometrial abnormalities in infertile women, assuming that the missing data mechanism is random, the amount of bias in calculating SEN and SPEC is very low in the diagnostic tests calculated before and after correction, using Begg and Greens and logistic regression method. But Zhou's method gives rather large biased estimates.