期刊
BIOMETRICS
卷 66, 期 4, 页码 999-1011出版社
WILEY-BLACKWELL PUBLISHING, INC
DOI: 10.1111/j.1541-0420.2009.01375.x
关键词
Accuracy; Competing risks; Cox regression; Discrimination; Kaplan-Meier estimator; Kernel smoothing; Prediction; Sensitivity; Specificity
资金
- NIH, National Institute of Environmental Health Sciences
- [UL1 RR025014]
- [R01 HL072966]
P>Competing risks arise naturally in time-to-event studies. In this article, we propose time-dependent accuracy measures for a marker when we have censored survival times and competing risks. Time-dependent versions of sensitivity or true positive (TP) fraction naturally correspond to consideration of either cumulative (or prevalent) cases that accrue over a fixed time period, or alternatively to incident cases that are observed among event-free subjects at any select time. Time-dependent (dynamic) specificity (1-false positive (FP)) can be based on the marker distribution among event-free subjects. We extend these definitions to incorporate cause of failure for competing risks outcomes. The proposed estimation for cause-specific cumulative TP/dynamic FP is based on the nearest neighbor estimation of bivariate distribution function of the marker and the event time. On the other hand, incident TP/dynamic FP can be estimated using a possibly nonproportional hazards Cox model for the cause-specific hazards and riskset reweighting of the marker distribution. The proposed methods extend the time-dependent predictive accuracy measures of Heagerty, Lumley, and Pepe (2000, Biometrics 56, 337-344) and Heagerty and Zheng (2005, Biometrics 61, 92-105).
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