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Quantifying the Predictive Performance of Prognostic Models for Censored Survival Data with Time-Dependent Covariates

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  • R. Schoop
  • E. Graf
  • M. Schumacher

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  • R. Schoop & E. Graf & M. Schumacher, 2008. "Quantifying the Predictive Performance of Prognostic Models for Censored Survival Data with Time-Dependent Covariates," Biometrics, The International Biometric Society, vol. 64(2), pages 603-610, June.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:2:p:603-610
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2007.00889.x
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    References listed on IDEAS

    as
    1. Yingye Zheng & Patrick Heagerty, 2004. "Semiparametric Estimation of Time-Dependent: ROC Curves for Longitudinal Marker Data," UW Biostatistics Working Paper Series 1052, Berkeley Electronic Press.
    2. Patrick J. Heagerty & Yingye Zheng, 2005. "Survival Model Predictive Accuracy and ROC Curves," Biometrics, The International Biometric Society, vol. 61(1), pages 92-105, March.
    3. Michael Schemper & Robin Henderson, 2000. "Predictive Accuracy and Explained Variation in Cox Regression," Biometrics, The International Biometric Society, vol. 56(1), pages 249-255, March.
    4. Yingye Zheng & Patrick J. Heagerty, 2005. "Partly Conditional Survival Models for Longitudinal Data," Biometrics, The International Biometric Society, vol. 61(2), pages 379-391, June.
    5. Patrick J. Heagerty & Thomas Lumley & Margaret S. Pepe, 2000. "Time-Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker," Biometrics, The International Biometric Society, vol. 56(2), pages 337-344, June.
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    Cited by:

    1. Marlena Maziarz & Patrick Heagerty & Tianxi Cai & Yingye Zheng, 2017. "On longitudinal prediction with time-to-event outcome: Comparison of modeling options," Biometrics, The International Biometric Society, vol. 73(1), pages 83-93, March.
    2. Giulia Barbati & Alessio Farcomeni, 2018. "Prognostic assessment of repeatedly measured time-dependent biomarkers, with application to dilated cardiomyopathy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(3), pages 545-557, August.
    3. Matthias Schmid & Thomas Hielscher & Thomas Augustin & Olaf Gefeller, 2011. "A Robust Alternative to the Schemper–Henderson Estimator of Prediction Error," Biometrics, The International Biometric Society, vol. 67(2), pages 524-535, June.
    4. P. Saha & P. J. Heagerty, 2010. "Time-Dependent Predictive Accuracy in the Presence of Competing Risks," Biometrics, The International Biometric Society, vol. 66(4), pages 999-1011, December.
    5. Shu Jiang & Yijun Xie & Graham A. Colditz, 2021. "Functional ensemble survival tree: Dynamic prediction of Alzheimer’s disease progression accommodating multiple time‐varying covariates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 66-79, January.
    6. Daniel Commenges & Benoit Liquet & Cécile Proust-Lima, 2012. "Choice of Prognostic Estimators in Joint Models by Estimating Differences of Expected Conditional Kullback–Leibler Risks," Biometrics, The International Biometric Society, vol. 68(2), pages 380-387, June.
    7. Jing Zhang & Jing Ning & Ruosha Li, 2023. "Evaluating Dynamic Discrimination Performance of Risk Prediction Models for Survival Outcomes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 353-371, July.
    8. Paul Blanche & Cécile Proust-Lima & Lucie Loubère & Claudine Berr & Jean-François Dartigues & Hélène Jacqmin-Gadda, 2015. "Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks," Biometrics, The International Biometric Society, vol. 71(1), pages 102-113, March.

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