title: Multiple imputation for correcting verification bias.
abstract: In the case in which all subjects are screened using a common test and only a subset of these subjects are tested using a golden standard test, it is well documented that there is a risk for bias, called verification bias. When the test has only two levels (e.g. positive and negative) and we are trying to estimate the sensitivity and specificity of the test, we are actually constructing a confidence interval for a binomial proportion. Since it is well documented that this estimation is not trivial even with complete data, we adopt multiple imputation framework for verification bias problem. We propose several imputation procedures for this problem and compare different methods of estimation. We show that our imputation methods are better than the existing methods with regard to nominal coverage and confidence interval length.
authors.affiliation: Department of Statistics, University of Connecticut, 215 Glenbrook Road Unit 4120 Storrs, CT 06269-4120, USA. oharel@stat.uconn.edu
authors.forename: Ofer
authors.initials: O
authors.lastname: Harel
authors.forename: Xiao-Hua
authors.initials: XH
authors.lastname: Zhou
mesh_terms: D015982:Bias; D001943:Breast Neoplasms / Q000175:diagnosis; D003198:Computer Simulation; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D003955:Diagnostic Tests, Routine / Q000592:standards*; D005260:Female; D006801:Humans; D008113:Liver Neoplasms / Q000175:diagnosis; D011877:Radionuclide Imaging; D012680:Sensitivity and Specificity; D014172:Transillumination