A Simple Modeling-free Method Provides Accurate Estimates of Sensitivity and Specificity of Longitudinal Disease Biomarkers

Objective: To assess the time-dependent accuracy of a continuous longitudinal biomarker used as a test for early diagnosis or prognosis. Methods: A method for accuracy assessment is proposed taking into account the marker measurement time and the delay between marker measurement and outcome. It dealt with markers having interval-censored measurements and a detection threshold. The threshold crossing times were assessed by a Bayesian method. A numerical study was conducted to test the procedures that were later applied to PCR measurements for prediction of cytomegalovirus disease after renal transplantation. Results: The Bayesian method corrected the bias induced by interval-censored measurements on sensitivity estimates, with corrections from 0.07 to 0.3. In the application to cytomegalovirus disease, the Bayesian method estimated the area under the ROC curve to be over 75% during the first 20 days after graft and within five days between marker measurement and disease onset. However, the accuracy decreased quickly as that delay increased and late after graft. Conclusions: The proposed Bayesian method is easy to implement for assessing the time-dependent accuracy of a longitudinal biomarker and gives unbiased results under some conditions.