Nonparametric estimation of time-dependent ROC curves conditional on a continuous covariate.
The receiver-operating characteristic (ROC) curve is the most widely used measure for evaluating the performance of a diagnostic biomarker when predicting a binary disease outcome. The ROC curve displays the true positive rate (or sensitivity) and the false positive rate (or 1-specificity) for different cut-off values used to classify an individual as healthy or diseased. In time-to-event studies, however, the disease status (e.g. death or alive) of an individual is not a fixed characteristic, and it varies along the study. In such cases, when evaluating the performance of the biomarker, several issues should be taken into account: first, the time-dependent nature of the disease status; and second, the presence of incomplete data (e.g. censored data typically present in survival studies). Accordingly, to assess the discrimination power of continuous biomarkers for time-dependent disease outcomes, time-dependent extensions of true positive rate, false positive rate, and ROC curve have been recently proposed. In this work, we present new nonparametric estimators of the cumulative/dynamic time-dependent ROC curve that allow accounting for the possible modifying effect of current or past covariate measures on the discriminatory power of the biomarker. The proposed estimators can accommodate right-censored data, as well as covariate-dependent censoring. The behavior of the estimators proposed in this study will be explored through simulations and illustrated using data from a cohort of patients who suffered from acute coronary syndrome.
Department of Statistics and Operations Research, and Biomedical Research Centre (CINBIO), University of Vigo, Campus Lagoas-Marcosende s/n, Vigo, 36310, Spain.
María Xosé
MX
Rodríguez-Álvarez
Centre of Mathematics and Department of Mathematics and Applications, University of Minho, Campus de Azurém, Guimarães, 4800-058, Portugal.
Luís
L
Meira-Machado
Department of Cardiology, University Clinical Hospital of Santiago de Compostela, Spain.
Emad
E
Abu-Assi
Department of Cardiology, University Clinical Hospital of Santiago de Compostela, Spain.
Sergio
S
Raposeiras-Roubín
D054058:Acute Coronary Syndrome / Q000175:diagnosis; D015415:Biomarkers / Q000032:analysis; D056808:Biostatistics; D003198:Computer Simulation; D005189:False Positive Reactions; D006801:Humans; D015233:Models, Statistical; D011237:Predictive Value of Tests; D012372:ROC Curve; D018709:Statistics, Nonparametric; D016019:Survival Analysis; D013997:Time Factors
D015415:Biomarkers
acute coronary syndrome; kernel-type smoothing; survival analysis; time-dependent ROC curve