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A Semiparametric Joint Model for Longitudinal and Survival Data with Application to Hemodialysis Study

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  • Liang Li
  • Bo Hu
  • Tom Greene

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  • 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.
    • Handle: RePEc:bla:biomet:v:65:y:2009:i:3:p:737-745
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2008.01168.x
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    References listed on IDEAS

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    1. Mengling Liu & Zhiliang Ying, 2007. "Joint Analysis of Longitudinal Data with Informative Right Censoring," Biometrics, The International Biometric Society, vol. 63(2), pages 363-371, June.
    2. Erning Li & Daowen Zhang & Marie Davidian, 2004. "Conditional Estimation for Generalized Linear Models When Covariates Are Subject-Specific Parameters in a Mixed Model for Longitudinal Measurements," Biometrics, The International Biometric Society, vol. 60(1), pages 1-7, March.
    3. Liang Li & Tom Greene, 2008. "Varying Coefficients Model with Measurement Error," Biometrics, The International Biometric Society, vol. 64(2), pages 519-526, June.
    4. 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.
    5. 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.
    6. Bin Nan & Xihong Lin & Lynda D. Lisabeth & Siobán D. Harlow, 2005. "A Varying-Coefficient Cox Model for the Effect of Age at a Marker Event on Age at Menopause," Biometrics, The International Biometric Society, vol. 61(2), pages 576-583, June.
    7. Sarah J. Ratcliffe & Wensheng Guo & Thomas R. Ten Have, 2004. "Joint Modeling of Longitudinal and Survival Data via a Common Frailty," Biometrics, The International Biometric Society, vol. 60(4), pages 892-899, December.
    8. Thomas Augustin, 2004. "An Exact Corrected Log‐Likelihood Function for Cox's Proportional Hazards Model under Measurement Error and Some Extensions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(1), pages 43-50, March.
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    Cited by:

    1. Wei Yang & Dawei Xie & Qiang Pan & Harold I. Feldman & Wensheng Guo, 2017. "Joint Modeling of Repeated Measures and Competing Failure Events in a Study of Chronic Kidney Disease," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 504-524, December.
    2. Dilip C. Nath & Atanu Bhattacharjee, 2014. "Joint longitudinal and survival data modelling: an application in anti-diabetes drug therapeutic effect," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 15(3), pages 437-452, June.
    3. Ram Thapa & Harold E. Burkhart & Jie Li & Yili Hong, 2016. "Modeling Clustered Survival Times of Loblolly Pine with Time-dependent Covariates and Shared Frailties," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(1), pages 92-110, March.
    4. 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.
    5. Walter Dempsey & Peter McCullagh, 2018. "Survival models and health sequences," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(4), pages 550-584, October.

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