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Estimating Survival Times Using Swiss Hospital Data


  • Kuhlmey, Florian

    () (University of Basel)

  • Minke, Matthias

    () (University of Basel)


We compare and evaluate two different approaches to estimate overall survival curvesfrom censored data of recurrent events: (1) standard survival time analysis, and (2) a multistate framework that explicitly estimates the mortality rate during censored periods. With both models, we estimate disease-specific survival curves with data from the Swiss Federal Statistical Office's medical statistics on hospitals (MedStat). Using cancer registry data as a benchmark for overall survival, we find that the accuracy of survival time estimates based on the multistate model are not superior to the simpler single-risk model. Although the computationally demanding multistate model is less accurate in predicting survival times, it may nevertheless be useful if intermediate transitions are the targeted issues.

Suggested Citation

  • Kuhlmey, Florian & Minke, Matthias, 2018. "Estimating Survival Times Using Swiss Hospital Data," Working papers 2018/14, Faculty of Business and Economics - University of Basel.
  • Handle: RePEc:bsl:wpaper:2018/14

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    References listed on IDEAS

    1. Laudicella, Mauro & Li Donni, Paolo & Smith, Peter C., 2013. "Hospital readmission rates: Signal of failure or success?," Journal of Health Economics, Elsevier, vol. 32(5), pages 909-921.
    2. Martini, Gianmaria & Berta, Paolo & Mullahy, John & Vittadini, Giorgio, 2014. "The effectiveness–efficiency trade-off in health care: The case of hospitals in Lombardy, Italy," Regional Science and Urban Economics, Elsevier, vol. 49(C), pages 217-231.
    3. Rahman, Momotazur & Norton, Edward C. & Grabowski, David C., 2016. "Do hospital-owned skilled nursing facilities provide better post-acute care quality?," Journal of Health Economics, Elsevier, vol. 50(C), pages 36-46.
    4. Mehdi Farsi & Geert Ridder, 2006. "Estimating the out-of-hospital mortality rate using patient discharge data," Health Economics, John Wiley & Sons, Ltd., vol. 15(9), pages 983-995.
    5. Daysal, N. Meltem, 2012. "Does uninsurance affect the health outcomes of the insured? Evidence from heart attack patients in California," Journal of Health Economics, Elsevier, vol. 31(4), pages 545-563.
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    More about this item


    Survival analysis; multistate-model; data simulation; hospital discharge data;

    JEL classification:

    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

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