Pulse lineResearch With Heart Logo

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

TitleEstimation of time-dependent area under the ROC curve for long-term risk prediction.
Publication TypeJournal Article
Year of Publication2006
AuthorsChambless LE, Diao G
JournalStat Med
Volume25
Issue20
Pagination3474-86
Date Published2006 Oct 30
ISSN0277-6715
KeywordsArea Under Curve, Risk Assessment, ROC Curve, Sensitivity and Specificity, Survival Analysis, Time Factors
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.

DOI10.1002/sim.2299
Alternate JournalStat Med
PubMed ID16220486
Grant ListN01-HC-55015 / HC / NHLBI NIH HHS / United States
N01-HC-55016 / HC / NHLBI NIH HHS / United States
N01-HC-55018 / HC / NHLBI NIH HHS / United States
N01-HC-55019 / HC / NHLBI NIH HHS / United States
N01-HC-55020 / HC / NHLBI NIH HHS / United States
N01-HC-55021 / HC / NHLBI NIH HHS / United States
N01-HC-55022 / HC / NHLBI NIH HHS / United States