The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Survival Model Predictive Accuracy and ROC Curves
Abstract
Summary The predictive accuracy of a survival model can be summarized using extensions of the proportion of variation explained by the model, orR2, commonly used for continuous response models, or using extensions of sensitivity and specificity, which are commonly used for binary response models. In this article we propose new time‐dependent accuracy summaries based on time‐specific versions of sensitivity and specificity calculated over risk sets. We connect the accuracy summaries to a previously proposed global concordance measure, which is a variant of Kendall's tau. In addition, we show how standard Cox regression output can be used to obtain estimates of time‐dependent sensitivity and specificity, and time‐dependent receiver operating characteristic (ROC) curves. Semiparametric estimation methods appropriate for both proportional and nonproportional hazards data are introduced, evaluated in simulations, and illustrated using two familiar survival data sets.
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Cites
Regression Models and Life-Tables
Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793
Agresti, A. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/0471249688 · Cited by 6,746
Proportional hazards tests and diagnostics based on weighted residuals
GRAMBSCH, P. M., & THERNEAU, T. M. (1994) · Biometrika · doi:10.1093/biomet/81.3.515 · Cited by 5,885 · cited 2×
“Grambsch and Therneau (1994) show that E{r * j | F[t (j) ]} ≈ [β(t) − β 0 ], where β 0 is the time-averaged coefficient and F(t) is the rightcontinuous filtration specifying the survival process history.”
Cited by
Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures
Steyerberg, E. W., Vickers, A. J., Cook, N. R., Gerds, T., Gonen, M., Obuchowski, N., Pencina, M. J., & Kattan, M. W. (2010) · Epidemiology · doi:10.1097/ede.0b013e3181c30fb2 · Cited by 5,170
Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008) · The Annals of Applied Statistics · doi:10.1214/08-aoas169 · Cited by 2,714
Alba, A. C., Agoritsas, T., Walsh, M., Hanna, S., Iorio, A., Devereaux, P. J., McGinn, T., & Guyatt, G. (2017) · JAMA · doi:10.1001/jama.2017.12126 · Cited by 1,631
Uno, H., Cai, T., Pencina, M. J., D'Agostino, R. B., & Wei, L. J. (2011) · Statistics in Medicine · doi:10.1002/sim.4154 · Cited by 1,535
A Multidimensional Index and Staging System for Idiopathic Pulmonary Fibrosis
Ley, B., Ryerson, C. J., Vittinghoff, E., Ryu, J. H., Tomassetti, S., Lee, J. S., Poletti, V., Buccioli, M., Elicker, B. M., Jones, K. D., et al. (2012) · Annals of Internal Medicine · doi:10.7326/0003-4819-156-10-201205150-00004 · Cited by 1,308
Metadata
- Authors
- Heagerty, P. J., & Zheng, Y.
- Venue
- Biometrics
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-02-28
- Volume
- 61
- Issue
- 1
- Pages
- 92-105
- ISSN
- 0006-341X (ISSN-L)
- 1541-0420
- OpenAlex
- W2109242212
- Cited by
- 1,464
- References
- 3
- FWCI
- 15.60
- Type
- journal article
- Language
- en
- Open access
- closed
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