Time‐dependent ROC curve estimation for interval‐censored data

• Published in: Biometrical journal Vol. 64; no. 6; pp. 1056 - 1074
• Main Authors: Beyene, Kassu Mehari, El Ghouch, Anouar
• Format: Journal Article
• Language: English
• Published: Germany Wiley - VCH Verlag GmbH & Co. KGaA 01.08.2022
• Subjects: AUC; bootstrap; Censored data (mathematics); Confidence intervals; cutoff value; Decompression; Decompression sickness; Graphical methods; kernel smoothing; Markers; Time dependence; Youden index; cutoff value; kernel smoothing; Youden index; bootstrap; AUC
• ISSN: 0323-3847; 1521-4036; 1521-4036
• DOI: 10.1002/bimj.202000382

The receiver‐operating characteristic (ROC) curve is the most popular graphical method for evaluating the classification accuracy of a diagnostic marker. In time‐to‐event studies, the subject's event status is time‐dependent, and hence, time‐dependent extensions of ROC curve have been proposed. However, in practice, the calculation of this curve is not straightforward due to the presence of censoring that may be of different types. Existing methods focus on the more standard and simple case of right‐censoring and neglect the general case of mixed interval‐censored data that may involve left‐, right‐, and interval‐censored observations. In this context, we propose and study a new time‐dependent ROC curve estimator. We also consider some summary measures (area under the ROC curve and Youden index) traditionally associated with ROC as well as the Youden‐based cutoff estimation method. The proposed method uses available data very efficiently. To this end, the unknown status (positive or negative) of censored subjects are estimated from the data via the estimation of the conditional survival function given the marker. For that, we investigate both model‐based and nonparametric approaches. We also provide variance estimates and confidence intervals using Bootstrap. A simulation study is conducted to investigate the finite sample behavior of the proposed methods and to compare their performance with a competitor. Globally, we observed better finite sample performances for the proposed estimators. Finally, we illustrate the methods using two data sets one from a hypobaric decompression sickness study and the other from an oral health study. The proposed methods are implemented in the R package cenROC.