Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/39116

TitleNonparametric estimation of time-dependent ROC curves conditional on a continuous covariate
Author(s)Rodríguez-Álvarez, María Xosé
Machado, Luís Meira
Abu-Assi, Emad
Raposeiras-Roubín, Sergio
KeywordsReceiver-operating characteristic (ROC) curve
Survival analysis
time-dependent ROC curve
kernel-type smoothing
acute coronary syndrome
Issue date2016
PublisherJohn Wiley and Sons
JournalStatistics in Medicine
CitationRodriguez-Alvarez, M. X., Meira-Machado, L., Abu-Assi, E., & Raposeiras-Roubin, S. (2016). Nonparametric estimation of time-dependent ROC curves conditional on a continuous covariate. Statistics in Medicine, 35(7), 1090-1102. doi: 10.1002/sim.6769
Abstract(s)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.
TypeArticle
URIhttp://hdl.handle.net/1822/39116
DOI10.1002/sim.6769
ISSN1097-0258
Publisher versionhttp://onlinelibrary.wiley.com/doi/10.1002/sim.6769/full
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CMAT - Artigos em revistas com arbitragem / Papers in peer review journals

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