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Joint analysis of correlated repeated measures and recurrent events processes in the presence of death, with application to a study on acquired immune deficiency syndrome

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  • Lei Liu
  • Xuelin Huang

Abstract

Summary. In many longitudinal studies, we observe two correlated processes: a repeated measures process and a recurrent events process, both subject to a dependent terminal event. For example, in the ‘Terry Beirn community programs for clinical research on AIDS’ (CPCRA) study, higher CD4 cell counts are associated with lower risk of recurrent opportunistic diseases. They are also correlated with mortality, e.g. higher CD4 cell repeated measures and a lower rate of opportunistic disease imply better survival for patients infected with the human immunodeficiency virus. We propose a joint random‐effects model for the three correlated outcomes. The correlation is modelled by conditioning on shared random effects. Covariate effects can be taken into account in the model. Maximum likelihood estimation and inference are carried out through a Gaussian quadrature technique, assuming piecewise constant baseline hazards for recurrent events and death. The model can be fitted conveniently by Gaussian quadrature tools, e.g. SAS procedure NLMIXED. Simulation studies show that the estimation method yields satisfactory results. We apply this method to the CPCRA data.

Suggested Citation

  • Lei Liu & Xuelin Huang, 2009. "Joint analysis of correlated repeated measures and recurrent events processes in the presence of death, with application to a study on acquired immune deficiency syndrome," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(1), pages 65-81, February.
  • Handle: RePEc:bla:jorssc:v:58:y:2009:i:1:p:65-81
    DOI: 10.1111/j.1467-9876.2008.00641.x
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    File URL: https://doi.org/10.1111/j.1467-9876.2008.00641.x
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    1. R. Henderson & P. Oman, 1999. "Effect of frailty on marginal regression estimates in survival analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 367-379, April.
    2. Chiung-Yu Huang & Mei-Cheng Wang, 2004. "Joint Modeling and Estimation for Recurrent Event Processes and Failure Time Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1153-1165, December.
    3. Lei Liu & Robert A. Wolfe & Xuelin Huang, 2004. "Shared Frailty Models for Recurrent Events and a Terminal Event," Biometrics, The International Biometric Society, vol. 60(3), pages 747-756, September.
    4. 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.
    5. Huang, Yijian & Wang, Mei-Cheng, 2003. "Frequency of Recurrent Events at Failure Time: Modeling and Inference," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 663-670, January.
    6. Lei Liu & Xuelin Huang & John O'Quigley, 2008. "Analysis of Longitudinal Data in the Presence of Informative Observational Times and a Dependent Terminal Event, with Application to Medical Cost Data," Biometrics, The International Biometric Society, vol. 64(3), pages 950-958, September.
    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. Wensheng Guo & Sarah J. Ratcliffe & Thomas Ten T. Have, 2004. "A Random Pattern-Mixture Model for Longitudinal Data With Dropouts," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 929-937, December.
    9. Jane Xu & Scott L. Zeger, 2001. "Joint analysis of longitudinal data comprising repeated measures and times to events," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 375-387.
    10. Zeng, Donglin & Lin, D.Y., 2007. "Semiparametric Transformation Models With Random Effects for Recurrent Events," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 167-180, March.
    11. Feng, Shibao & Wolfe, Robert A. & Port, Friedrich K., 2005. "Frailty Survival Model Analysis of the National Deceased Donor Kidney Transplant Dataset Using Poisson Variance Structures," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 728-735, September.
    12. D. Zeng & D. Y. Lin, 2007. "Maximum likelihood estimation in semiparametric regression models with censored data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(4), pages 507-564, September.
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    Cited by:

    1. M. H. Hof & J. Z. Musoro & R. B. Geskus & G. H. Struijk & I. J. M. ten Berge & A. H. Zwinderman, 2017. "Simulated maximum likelihood estimation in joint models for multiple longitudinal markers and recurrent events of multiple types, in the presence of a terminal event," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(15), pages 2756-2777, November.
    2. Qing Cai & Mei‐Cheng Wang & Kwun Chuen Gary Chan, 2017. "Joint modeling of longitudinal, recurrent events and failure time data for survivor's population," Biometrics, The International Biometric Society, vol. 73(4), pages 1150-1160, December.
    3. Shanshan Li, 2016. "Joint modeling of recurrent event processes and intermittently observed time-varying binary covariate processes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(1), pages 145-160, January.
    4. 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.
    5. Na Cai & Wenbin Lu & Hao Helen Zhang, 2012. "Time-Varying Latent Effect Model for Longitudinal Data with Informative Observation Times," Biometrics, The International Biometric Society, vol. 68(4), pages 1093-1102, December.
    6. Gongjun Xu & Sy Han Chiou & Chiung-Yu Huang & Mei-Cheng Wang & Jun Yan, 2017. "Joint Scale-Change Models for Recurrent Events and Failure Time," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 794-805, April.
    7. Yang-Jin Kim, 2014. "Regression analysis of recurrent events data with incomplete observation gaps," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(7), pages 1619-1626, July.
    8. Liu, Yue & Liu, Lei & Zhou, Jianhui, 2015. "Joint latent class model of survival and longitudinal data: An application to CPCRA study," Computational Statistics & Data Analysis, Elsevier, vol. 91(C), pages 40-50.

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