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A joint longitudinal and survival model with health care usage for insured elderly


  • Xavier Piulachs

    () (Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona)

  • Ramon Alemany

    () (Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona)

  • Montserrat Guillen

    () (Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona)


We study longevity and usage of medical resources of a sample of individuals aged 65 years or more who are covered by a private insurance policy. A longitudinal analysis is presented, where the yearly cumulative number of medical coverage requests by each subject characterizes insurance intensity of care until death. We confirm that there is a significant correlation between the longitudinal data on usage level and the survival time processes. We obtain dynamic estimations of event probabilities and we exploit the potential of joint models for personalized survival curve adjustment.

Suggested Citation

  • Xavier Piulachs & Ramon Alemany & Montserrat Guillen, 2014. "A joint longitudinal and survival model with health care usage for insured elderly," Working Papers 2014-07, Universitat de Barcelona, UB Riskcenter.
  • Handle: RePEc:bak:wpaper:201407

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    References listed on IDEAS

    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. Gómez-Puig, Marta & Sosvilla-Rivero, Simón, 2014. "Causality and contagion in EMU sovereign debt markets," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 12-27.
    3. Blane, David & Netuveli, Gopalakrishnan & Montgomery, Scott M., 2008. "Quality of life, health and physiological status and change at older ages," Social Science & Medicine, Elsevier, vol. 66(7), pages 1579-1587, April.
    4. Rizopoulos, Dimitris, 2012. "Fast fitting of joint models for longitudinal and event time data using a pseudo-adaptive Gaussian quadrature rule," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 491-501.
    5. Marta Gómez-Puig & Simón Sosvilla-Rivero, 2014. "“EMU sovereign debt market crisis: Fundamentals-based or pure contagion?”," IREA Working Papers 201402, University of Barcelona, Research Institute of Applied Economics, revised May 2014.
    6. Francisca Galindo Garre & Aeilko H. Zwinderman & Ronald B. Geskus & Yvo W. J. Sijpkens, 2008. "A joint latent class changepoint model to improve the prediction of time to graft failure," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 299-308, January.
    7. 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.
    8. Michael Murphy, 2012. "Proximity to Death and Health Care Costs," Chapters, in: Alistair McGuire & Joan Costa-Font (ed.),The LSE Companion to Health Policy, chapter 13, Edward Elgar Publishing.
    9. Ha Dao & Luc Godbout & Pierre Fortin, 2014. "On the Importance of Taking End-of-Life Expenditures into Account when Projecting Health-Care Spending," Canadian Public Policy, University of Toronto Press, vol. 40(1), pages 45-56, March.
    10. 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).
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    Cited by:

    1. Manuela Alcañiz & Aïda Solé-Auró, 2018. "Ageing and health-related quality of life: evidence from Catalonia (Spain)," Working Papers 2018-01, Universitat de Barcelona, UB Riskcenter.
    2. Mercedes Ayuso & Montserrat Guillen & Jens Perch Nielsen, 2019. "Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data," Transportation, Springer, vol. 46(3), pages 735-752, June.

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    joint modeling; health insurance; personalized survival probability; longitudinal data;

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