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Cited by 311

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Estimation of time‐dependent area under the ROC curve for long‐term risk prediction

Chambless, Lloyd E., Diao, Guoqing·2006·Statistics in Medicine
DOI 10.1002/sim.2299
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Abstract

Sensitivity, specificity, and area under the ROC curve (AUC) are often used to measure the ability of survival models to predict future risk. Estimation of these parameters is complicated by the fact that these parameters are time‐dependent and by the fact that censoring affects their estimation just as it affects estimation of survival curves or coefficients of survival regression models. The authors present several estimators that overcome these complications. One approach is a recursive calculation over the ordered times of events, analogous to the Kaplan–Meier approach to survival function estimation. Another is to first apply Bayes' theorem to write the parameters of interest in terms of conditional survival functions that are then estimated by survival analysis methods. Simulation studies demonstrate that the proposed estimators perform well in practical situations, when compared with an estimator (c‐statistic, from logistic regression) that ignores time. An illustration with data from a cardiovascular follow‐up study is provided. Copyright © 2005 John Wiley & Sons, Ltd.

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Metadata

Authors
Chambless, L. E., & Diao, G.
Venue
Statistics in Medicine
Publisher
Wiley
Published
2005-10-11
Volume
25
Issue
20
Pages
3474-3486
ISSN
  • 0277-6715 (ISSN-L)
  • 1097-0258
DOI
10.1002/sim.2299
OpenAlex
W2005060666
Cited by
311
References
6
FWCI
5.62
Type
journal article
Language
en
Open access
closed

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