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 diffe...

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Bibliographic Details
Published in:Statistics in medicine 2016-03, Vol.35 (7), p.1090-1102
Main Authors: Rodríguez-Álvarez, María Xosé, Meira-Machado, Luís, Abu-Assi, Emad, Raposeiras-Roubín, Sergio
Format: Article
Language:English
Subjects:
acute coronary syndrome
Acute Coronary Syndrome - diagnosis
Biomarkers
Biomarkers - analysis
Biostatistics
Computer Simulation
Estimating techniques
False Positive Reactions
Humans
kernel-type smoothing
Medical diagnosis
Medical statistics
Models, Statistical
Predictive Value of Tests
ROC Curve
Statistics, Nonparametric
Survival Analysis
Time Factors
time-dependent ROC curve
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Summary: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. Copyright © 2015 John Wiley & Sons, Ltd.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.6769