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Correction of Verication Bias using Log-linear Models for a Single Binaryscale Diagnostic Tests
Journal of Biometrics & Biostatistics
  • Haresh Rochani, Georgia Southern University
  • Hani M. Samawi, Georgia Southern University
  • Robert L. Vogel, Georgia Southern University
  • Jingjing Yin, Georgia Southern University
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In diagnostic medicine, the test that determines the true disease status without an error is referred to as the gold standard. Even when a gold standard exists, it is extremely difficult to verify each patient due to the issues of costeffectiveness and invasive nature of the procedures. In practice some of the patients with test results are not selected for verification of the disease status which results in verification bias for diagnostic tests. The ability of the diagnostic test to correctly identify the patients with and without the disease can be evaluated by measures such as sensitivity, specificity and predictive values. However, these measures can give biased estimates if we only consider the patients with test results who also underwent the gold standard procedure. The emphasis of this paper is to apply the log-linear model approach to compute the maximum likelihood estimates for sensitivity, specificity and predictive values. We also compare the estimates with Zhou’s results and apply this approach to analyze Hepatic Scintigraph data under the assumption of ignorable as well as non-ignorable missing data mechanisms. We demonstrated the efficiency of the estimators by using simulation studies.


©2015 Rochania HD, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Citation Information
Haresh Rochani, Hani M. Samawi, Robert L. Vogel and Jingjing Yin. "Correction of Verication Bias using Log-linear Models for a Single Binaryscale Diagnostic Tests" Journal of Biometrics & Biostatistics Vol. 6 Iss. 5 (2015) p. 266
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