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Dynamic clustering of hazard functions: an application to disease progression in chronic heart failure

Author

Listed:
  • Francesca Ieva

    (Università degli Studi di Milano)

  • Anna Maria Paganoni

    (Politecnico di Milano)

  • Teresa Pietrabissa

    (Politecnico di Milano)

Abstract

We analyse data collected from the administrative datawarehouse of an Italian regional district (Lombardia) concerning patients affected by Chronic Heart Failure. The longitudinal data gathering for each patient hospital readmissions in time, as well as patient-specific covariates, is studied as a realization of non homogeneous Poisson process. Since the aim behind this study is to identify groups of patients behaving similarly in terms of disease progression and then healthcare consumption, we conjectured the time segments between two consecutive hospitalizations to be Weibull distributed in each hidden cluster. Adding a frailty term to take into account the within subjects unknown variability, the corresponding patient-specific hazard functions are reconstructed. Therefore, the comprehensive distribution for each time to event variable is modelled as a Weibull Mixture. We are then able to easily interpret the related hidden groups as healthy, sick, and terminally ill subjects.

Suggested Citation

  • Francesca Ieva & Anna Maria Paganoni & Teresa Pietrabissa, 2017. "Dynamic clustering of hazard functions: an application to disease progression in chronic heart failure," Health Care Management Science, Springer, vol. 20(3), pages 353-364, September.
    • Handle: RePEc:kap:hcarem:v:20:y:2017:i:3:d:10.1007_s10729-016-9357-3
    DOI: 10.1007/s10729-016-9357-3
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    References listed on IDEAS

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    1. Issac Shams & Saeede Ajorlou & Kai Yang, 2015. "A predictive analytics approach to reducing 30-day avoidable readmissions among patients with heart failure, acute myocardial infarction, pneumonia, or COPD," Health Care Management Science, Springer, vol. 18(1), pages 19-34, March.
    2. Patrick Mair & Marcus Hudec, 2009. "Multivariate Weibull mixtures with proportional hazard restrictions for dwell‐time‐based session clustering with incomplete data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(5), pages 619-639, December.
    3. Mollie Shulan & Kelly Gao & Crystal Moore, 2013. "Predicting 30-day all-cause hospital readmissions," Health Care Management Science, Springer, vol. 16(2), pages 167-175, June.
    4. Paolo Berta & Chiara Seghieri & Giorgio Vittadini, 2013. "Comparing health outcomes among hospitals: the experience of the Lombardy Region," Health Care Management Science, Springer, vol. 16(3), pages 245-257, September.
    5. Rondeau, Virginie & Marzroui, Yassin & Gonzalez, Juan R., 2012. "frailtypack: An R Package for the Analysis of Correlated Survival Data with Frailty Models Using Penalized Likelihood Estimation or Parametrical Estimation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i04).
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    Cited by:

    1. Saligrama Agnihothri & Leon Cui & Mohammad Delasay & Balaraman Rajan, 2020. "The value of mHealth for managing chronic conditions," Health Care Management Science, Springer, vol. 23(2), pages 185-202, June.

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