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, one is 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 (MI) framework for verification bias problem. We propose several proper imputation procedures for this problem, and compare different methods of estimation. We show that our imputation methods are doing much better then the existing methods with regard to nominal coverage and confidence interval length.
Biostatistics | Statistical Methodology | Statistical Theory
Harel, Ofer and Zhou, Xiao-Hua, "Multiple Imputation for Correcting Verification Bias" (May 2005). UW Biostatistics Working Paper Series. Working Paper 252.