Non-parametric estimation of a time-dependent predictive accuracy curve.
A major biomedical goal associated with evaluating a candidate biomarker or developing a predictive model score for event-time outcomes is to accurately distinguish between incident cases from the controls surviving beyond t throughout the entire study period. Extensions of standard binary classification measures like time-dependent sensitivity, specificity, and receiver operating characteristic (ROC) curves have been developed in this context (Heagerty, P. J., and others, 2000. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56, 337-344). We propose a direct, non-parametric method to estimate the time-dependent Area under the curve (AUC) which we refer to as the weighted mean rank (WMR) estimator. The proposed estimator performs well relative to the semi-parametric AUC curve estimator of Heagerty and Zheng (2005. Survival model predictive accuracy and ROC curves. Biometrics 61, 92-105). We establish the asymptotic properties of the proposed estimator and show that the accuracy of markers can be compared very simply using the difference in the WMR statistics. Estimators of pointwise standard errors are provided.
Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA. paramita.sahachaudhuri@duke.edu
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D019540:Area Under Curve; D015415:Biomarkers / Q000032:analysis*; D001943:Breast Neoplasms / Q000378:metabolism / Q000473:pathology; D002453:Cell Cycle / Q000502:physiology; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D005434:Flow Cytometry; D006801:Humans; D008105:Liver Cirrhosis, Biliary / Q000188:drug therapy / Q000378:metabolism; D010396:Penicillamine / Q000627:therapeutic use; D011237:Predictive Value of Tests
D015415:Biomarkers; D010396:Penicillamine