Imputation approaches for estimating diagnostic accuracy for multiple tests from partially verified designs

Abstract

Interest often focuses on estimating sensitivity and specificity of a group of raters or a set of new diagnostic tests in situations in which gold standard evaluation is expensive or invasive. Various authors have proposed semilatent class modeling approaches for estimating diagnostic accuracy in this situation. This article presents imputation approaches for this problem. I show how imputation provides a simpler way of performing diagnostic accuracy and prevalence estimation than the use of semilatent modeling. Furthermore, the imputation approach is more robust to modeling assumptions and, in general, there is only a moderate efficiency loss relative to a correctly specified semilatent class model. I apply imputation to a study designed to estimate the diagnostic accuracy of digital radiography for gastric cancer. The feasibility and robustness of imputation is illustrated with analysis, asymptotic results, and simulations.