title: Bias in estimating accuracy of a binary screening test with differential disease verification.
abstract: Sensitivity, specificity, positive and negative predictive value are typically used to quantify the accuracy of a binary screening test. In some studies, it may not be ethical or feasible to obtain definitive disease ascertainment for all subjects using a gold standard test. When a gold standard test cannot be used, an imperfect reference test that is less than 100 per cent sensitive and specific may be used instead. In breast cancer screening, for example, follow-up for cancer diagnosis is used as an imperfect reference test for women where it is not possible to obtain gold standard results. This incomplete ascertainment of true disease, or differential disease verification, can result in biased estimates of accuracy. In this paper, we derive the apparent accuracy values for studies subject to differential verification. We determine how the bias is affected by the accuracy of the imperfect reference test, the percent who receive the imperfect reference standard test not receiving the gold standard, the prevalence of the disease, and the correlation between the results for the screening test and the imperfect reference test. It is shown that designs with differential disease verification can yield biased estimates of accuracy. Estimates of sensitivity in cancer screening trials may be substantially biased. However, careful design decisions, including selection of the imperfect reference test, can help to minimize bias. A hypothetical breast cancer screening study is used to illustrate the problem.
authors.affiliation: Division of Biostatistics, University of Southern California, Keck School of Medicine, Arcadia, CA 91006, U.S.A.. talonzo@childrensoncologygroup.org
authors.forename: Todd A
authors.initials: TA
authors.lastname: Alonzo
authors.forename: John T
authors.initials: JT
authors.lastname: Brinton
authors.forename: Brandy M
authors.initials: BM
authors.lastname: Ringham
authors.forename: Deborah H
authors.initials: DH
authors.lastname: Glueck
mesh_terms: D015982:Bias; D001706:Biopsy / Q000592:standards / Q000706:statistics & numerical data*; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000453:epidemiology; D003198:Computer Simulation; D003937:Diagnosis, Differential; D003951:Diagnostic Errors / Q000706:statistics & numerical data*; D055088:Early Detection of Cancer / Q000379:methods / Q000592:standards / Q000706:statistics & numerical data; D015340:Epidemiologic Research Design; D005260:Female; D006801:Humans; D008327:Mammography / Q000592:standards / Q000706:statistics & numerical data*; D012680:Sensitivity and Specificity