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On longitudinal prediction with time-to-event outcome: Comparison of modeling options

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  • Marlena Maziarz
  • Patrick Heagerty
  • Tianxi Cai
  • Yingye Zheng

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  • 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.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:1:p:83-93
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    File URL: http://hdl.handle.net/10.1111/biom.12562
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    References listed on IDEAS

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    1. Dimitris Rizopoulos, 2011. "Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 67(3), pages 819-829, September.
    2. 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.
    3. Yingye Zheng & Tianxi Cai & Ziding Feng, 2006. "Application of the Time-Dependent ROC Curves for Prognostic Accuracy with Multiple Biomarkers," Biometrics, The International Biometric Society, vol. 62(1), pages 279-287, March.
    4. 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.
    5. Hans C. Van Houwelingen, 2007. "Dynamic Prediction by Landmarking in Event History Analysis," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(1), pages 70-85, March.
    6. Rizopoulos, Dimitris, 2010. "JM: An R Package for the Joint Modelling of Longitudinal and Time-to-Event Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i09).
    7. 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.
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    Cited by:

    1. Kamaryn T. Tanner & Linda D. Sharples & Rhian M. Daniel & Ruth H. Keogh, 2021. "Dynamic survival prediction combining landmarking with a machine learning ensemble: Methodology and empirical comparison," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 3-30, January.
    2. Wang, Shikun & Li, Zhao & Lan, Lan & Zhao, Jieyi & Zheng, W. Jim & Li, Liang, 2022. "GPU accelerated estimation of a shared random effect joint model for dynamic prediction," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    3. Yunda Huang & Yuanyuan Zhang & Zong Zhang & Peter B. Gilbert, 2020. "Generating Survival Times Using Cox Proportional Hazards Models with Cyclic and Piecewise Time-Varying Covariates," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(3), pages 324-339, December.
    4. Marvin N. Wright & Sasmita Kusumastuti & Laust H. Mortensen & Rudi G. J. Westendorp & Thomas A. Gerds, 2021. "Personalised need of care in an ageing society: The making of a prediction tool based on register data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1199-1219, October.

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