Weighted generalized score test for comparing predictive values in the presence of verification bias

Positive and negative predictive values of a diagnostic test are two important measures of test accuracy, which are more relevant in clinical settings than sensitivity and specificity. Statistical methods have been well-developed to compare the predictive values of two binary diagnostic tests when test results and disease status fully observed for all study patients. In practice, however, it is common that only a subset of study patients have the disease status verified due to ethical or cost considerations. Methods applied directly to the verified subjects may lead to biased results. A bias-corrected method has been developed to compare two predictive values in the presence of verification bias. However, the complexity of the existing method and the computational difficulty in implementing it has restricted its use. A simple and easily implemented statistical method is therefore needed. In this paper, we propose a weighted generalized score (WGS) test statistic for comparing two predictive values in the presence of verification bias. The proposed WGS test statistic is intuitive and simple to compute, only involving some minor modification of the WGS test statistic when disease status is verified for each study patient. Simulations demonstrate that the proposed WGS test statistic preserves type I error much better than the existing Wald statistic. The method is illustrated with data from a study of methods for the diagnosis of coronary artery disease.