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Prospective Accuracy for Longitudinal Markers

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  • Yingye Zheng
  • Patrick J. Heagerty

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  • Yingye Zheng & Patrick J. Heagerty, 2007. "Prospective Accuracy for Longitudinal Markers," Biometrics, The International Biometric Society, vol. 63(2), pages 332-341, June.
  • Handle: RePEc:bla:biomet:v:63:y:2007:i:2:p:332-341
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2006.00726.x
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    References listed on IDEAS

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    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. P. J. Heagerty & M. S. Pepe, 1999. "Semiparametric estimation of regression quantiles with application to standardizing weight for height and age in US children," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(4), pages 533-551.
    4. Stuart R. Lipsitz & Garrett M. Fitzmaurice & Joseph G. Ibrahim & Richard Gelber & Steven Lipshultz, 2002. "Parameter Estimation in Longitudinal Studies with Outcome-Dependent Follow-Up," Biometrics, The International Biometric Society, vol. 58(3), pages 621-630, September.
    5. Haiqun Lin & Daniel O. Scharfstein & Robert A. Rosenheck, 2004. "Analysis of longitudinal data with irregular, outcome‐dependent follow‐up," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 791-813, August.
    6. 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.
    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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    Citations

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    Cited by:

    1. 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.
    2. Liang Li & Sheng Luo & Bo Hu & Tom Greene, 0. "Dynamic Prediction of Renal Failure Using Longitudinal Biomarkers in a Cohort Study of Chronic Kidney Disease," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 0, pages 1-22.
    3. Liang Li & Sheng Luo & Bo Hu & Tom Greene, 2017. "Dynamic Prediction of Renal Failure Using Longitudinal Biomarkers in a Cohort Study of Chronic Kidney Disease," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 357-378, December.
    4. Yi Li & Lu Tian & Lee-Jen Wei, 2011. "Estimating Subject-Specific Dependent Competing Risk Profile with Censored Event Time Observations," Biometrics, The International Biometric Society, vol. 67(2), pages 427-435, June.
    5. Yingye Zheng & Tianxi Cai & Yuying Jin & Ziding Feng, 2012. "Evaluating Prognostic Accuracy of Biomarkers under Competing Risk," Biometrics, The International Biometric Society, vol. 68(2), pages 388-396, June.
    6. Dimitris Rizopoulos & Laura A. Hatfield & Bradley P. Carlin & Johanna J. M. Takkenberg, 2014. "Combining Dynamic Predictions From Joint Models for Longitudinal and Time-to-Event Data Using Bayesian Model Averaging," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1385-1397, December.
    7. 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.
    8. C. Jason Liang & Patrick J. Heagerty, 2017. "Rejoinder to discussions on: A risk-based measure of time-varying prognostic discrimination for survival models," Biometrics, The International Biometric Society, vol. 73(3), pages 745-748, September.

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