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Dynamic Prediction of Renal Failure Using Longitudinal Biomarkers in a Cohort Study of Chronic Kidney Disease


  • Liang Li

    () (University of Texas MD Anderson Cancer Center)

  • Sheng Luo

    (University of Texas School of Public Health)

  • Bo Hu

    (Cleveland Clinic)

  • Tom Greene

    (University of Utah)


In longitudinal studies, prognostic biomarkers are often measured longitudinally. It is of both scientific and clinical interest to predict the risk of clinical events, such as disease progression or death, using these longitudinal biomarkers as well as other time-dependent and time-independent information about the patient. The prediction is dynamic in the sense that it can be made at any time during the follow-up, adapting to the changing at-risk population and incorporating the most recent longitudinal data. One approach is to build a joint model of longitudinal predictor variables and time to the clinical event, and draw predictions from the posterior distribution of the time to event conditional on longitudinal history. Another approach is to use the landmark model, which is a system of prediction models that evolve with the follow-up time. We review the pros and cons of the two approaches and present a general analytical framework using the landmark approach. The proposed framework allows the measurement times of longitudinal data to be irregularly spaced and differ between subjects. We propose a unified kernel weighting approach for estimating the model parameters, calculating the predicted probabilities, and evaluating the prediction accuracy through double time-dependent receiver operating characteristic curves. We illustrate the proposed analytical framework using the African American study of kidney disease and hypertension to develop a landmark model for dynamic prediction of end-stage renal diseases or death among patients with chronic kidney disease.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:stabio:v:9:y:2017:i:2:d:10.1007_s12561-016-9183-7
    DOI: 10.1007/s12561-016-9183-7

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    References listed on IDEAS

    1. Yingye Zheng & Patrick J. Heagerty, 2007. "Prospective Accuracy for Longitudinal Markers," Biometrics, The International Biometric Society, vol. 63(2), pages 332-341, June.
    2. Jeremy M. G. Taylor & Yongseok Park & Donna P. Ankerst & Cecile Proust-Lima & Scott Williams & Larry Kestin & Kyoungwha Bae & Tom Pickles & Howard Sandler, 2013. "Real-Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models," Biometrics, The International Biometric Society, vol. 69(1), pages 206-213, March.
    3. Sun, Jianguo & Sun, Liuquan & Liu, Dandan, 2007. "Regression Analysis of Longitudinal Data in the Presence of Informative Observation and Censoring Times," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1397-1406, December.
    4. Qi Gong & Douglas E. Schaubel, 2013. "Partly Conditional Estimation of the Effect of a Time-Dependent Factor in the Presence of Dependent Censoring," Biometrics, The International Biometric Society, vol. 69(2), pages 338-347, June.
    5. Liang Li & Tom Greene, 2008. "Varying Coefficients Model with Measurement Error," Biometrics, The International Biometric Society, vol. 64(2), pages 519-526, June.
    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.
    8. 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.
    9. J. B. Copas, 2002. "Overestimation of the receiver operating characteristic curve for logistic regression," Biometrika, Biometrika Trust, vol. 89(2), pages 315-331, June.
    10. Fushing Hsieh & Yi-Kuan Tseng & Jane-Ling Wang, 2006. "Joint Modeling of Survival and Longitudinal Data: Likelihood Approach Revisited," Biometrics, The International Biometric Society, vol. 62(4), pages 1037-1043, December.
    11. Zhang, Wenyang & Lee, Sik-Yum & Song, Xinyuan, 2002. "Local Polynomial Fitting in Semivarying Coefficient Model," Journal of Multivariate Analysis, Elsevier, vol. 82(1), pages 166-188, July.
    12. Dimitris Rizopoulos & Geert Verbeke & Emmanuel Lesaffre, 2009. "Fully exponential Laplace approximations for the joint modelling of survival and longitudinal data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 637-654, June.
    13. Liang Li & Bo Hu & Tom Greene, 2009. "A Semiparametric Joint Model for Longitudinal and Survival Data with Application to Hemodialysis Study," Biometrics, The International Biometric Society, vol. 65(3), pages 737-745, September.
    14. 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.
    15. 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.
    16. Yi-Kuan Tseng & Fushing Hsieh & Jane-Ling Wang, 2005. "Joint modelling of accelerated failure time and longitudinal data," Biometrika, Biometrika Trust, vol. 92(3), pages 587-603, September.
    17. Layla Parast & Su-Chun Cheng & Tianxi Cai, 2012. "Landmark Prediction of Long-Term Survival Incorporating Short-Term Event Time Information," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1492-1501, December.
    18. 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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