Smoothed time‐dependent receiver operating characteristic curve for right censored survival data

• Published in: Statistics in medicine Vol. 39; no. 24; pp. 3373 - 3396
• Main Authors: Beyene, Kassu Mehari, El Ghouch, Anouar
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 30.10.2020
• Subjects: AUC; bandwidth selection; kernel estimation; Random variables; sensitivity and specificity; weighted distribution; weighted distribution; kernel estimation; sensitivity and specificity; bandwidth selection; AUC
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.8671

The prediction reliability is of primary concern in many clinical studies when the objective is to develop new predictive models or improve existing risk scores. In fact, before using a model in any clinical decision making, it is very important to check its ability to discriminate between subjects who are at risk of, for example, developing certain disease in a near future from those who will not. To that end, the time‐dependent receiver operating characteristic (ROC) curve is the most commonly used method in practice. Several approaches have been proposed in the literature to estimate the ROC nonparametrically in the context of survival data. But, except one recent approach, all the existing methods provide a nonsmooth ROC estimator whereas, by definition, the ROC curve is smooth. In this article we propose and study a new nonparametric smooth ROC estimator based on a weighted kernel smoother. More precisely, our approach relies on a well‐known kernel method used to estimate cumulative distribution functions of random variables with bounded supports. We derived some asymptotic properties for the proposed estimator. As bandwidth is the main parameter to be set, we present and study different methods to appropriately select one. A simulation study is conducted, under different scenarios, to prove the consistency of the proposed method and to compare its finite sample performance with a competitor. The results show that the proposed method performs better and appear to be quite robust to bandwidth choice. As for inference purposes, our results also reveal the good performances of a proposed nonparametric bootstrap procedure. Furthermore, we illustrate the method using a real data example.