Accounting for Nonignorable Verification Bias in Assessment of Diagnostic Tests

• Published in: Biometrics Vol. 59; no. 1; pp. 163 - 171
• Main Authors: Kosinski, Andrzej S., Barnhart, Huiman X.
• Format: Journal Article
• Language: English
• Published: 350 Main Street , Malden , MA 02148 , USA , and 9600 Garsington Road , Oxford OX4 2DQ , UK Blackwell Publishing, Inc 01.03.2003; International Biometric Society
• Subjects: Aged; Biometrics; biometry; computer software; Consultant's Forum; Coronary artery disease; Coronary Artery Disease - diagnosis; Diagnostic Errors; diagnostic techniques; Diagnostic test; Diagnostic Tests, Routine - standards; Disease models; Female; Gold standard; Humans; Likelihood Functions; Logistic Models; Male; Medical diagnostic tests; Middle Aged; Missing data; Nonignorable missing data; Parametric models; patients; Predisposing factors; regression analysis; risk factors; Selection Bias; Sensitivity and Specificity; Thallium; Tomography, Emission-Computed, Single-Photon - methods; Tomography, Emission-Computed, Single-Photon - standards; Verification bias
• ISSN: 0006-341X; 1541-0420
• DOI: 10.1111/1541-0420.00019

A “gold” standard test, providing definitive verification of disease status, may be quite invasive or expensive. Current technological advances provide less invasive, or less expensive, diagnostic tests. Ideally, a diagnostic test is evaluated by comparing it with a definitive gold standard test. However, the decision to perform the gold standard test to establish the presence or absence of disease is often influenced by the results of the diagnostic test, along with other measured, or not measured, risk factors. If only data from patients who received the gold standard test were used to assess the test performance, the commonly used measures of diagnostic test performance—sensitivity and specificity—are likely to be biased. Sensitivity would often be higher, and specificity would be lower, than the true values. This bias is called verification bias. Without adjustment for verification bias, one may possibly introduce into the medical practice a diagnostic test with apparent, but not truly, high sensitivity. In this article, verification bias is treated as a missing covariate problem. We propose a flexible modeling and computational framewzork for evaluating the performance of a diagnostic test, with adjustment for nonignorable verification bias. The presented computational method can be utilized with any software that can repetitively use a logistic regression module. The approach is likelihood‐based, and allows use of categorical or continuous covariates. An explicit formula for the observed information matrix is presented, so that one can easily compute standard errors of estimated parameters. The methodology is illustrated with a cardiology data example. We perform a sensitivity analysis of the dependency of verification selection process on disease.