IDEAS home Printed from https://ideas.repec.org/p/bsl/wpaper/2018-14.html
   My bibliography  Save this paper

Estimating Survival Times Using Swiss Hospital Data

Author

Listed:
  • Kuhlmey, Florian

    (University of Basel)

  • Minke, Matthias

    (University of Basel)

Abstract

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
    as

    Download full text from publisher

    File URL: https://edoc.unibas.ch/64724/1/20180618101344_5b2769b860475.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    5. repec:imp:wpaper:9224 is not listed on IDEAS
    6. 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, September.
    7. Wang M-C. & Qin J. & Chiang C-T., 2001. "Analyzing Recurrent Event Data With Informative Censoring," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1057-1065, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Moscone, Francesco & Siciliani, Luigi & Tosetti, Elisa & Vittadini, Giorgio, 2020. "Do public and private hospitals differ in quality? Evidence from Italy," Regional Science and Urban Economics, Elsevier, vol. 83(C).
    2. Anne-Fleur Roos & Eddy van Doorslaer & Owen O'Donnell & Erik Schut & Marco Varkevisser, 2018. "Does price competition damage healthcare quality?," Tinbergen Institute Discussion Papers 18-040/V, Tinbergen Institute.
    3. Guccio, C. & Lisi, D. & Martorana, M.F. & Pignataro, G., 2020. "Incorporating quality in the efficiency assessment of hospitals using a generalized directional distance function approach," Health, Econometrics and Data Group (HEDG) Working Papers 20/17, HEDG, c/o Department of Economics, University of York.
    4. Xiaowei Sun & Jieli Ding & Liuquan Sun, 2020. "A semiparametric additive rates model for the weighted composite endpoint of recurrent and terminal events," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 471-492, July.
    5. Brekke, Kurt R. & Canta, Chiara & Siciliani, Luigi & Straume, Odd Rune, 2021. "Hospital competition in a national health service: Evidence from a patient choice reform," Journal of Health Economics, Elsevier, vol. 79(C).
    6. Ferreira, D.C. & Marques, R.C., 2019. "Do quality and access to hospital services impact on their technical efficiency?," Omega, Elsevier, vol. 86(C), pages 218-236.
    7. Jones, A.M, 2010. "Models For Health Care," Health, Econometrics and Data Group (HEDG) Working Papers 10/01, HEDG, c/o Department of Economics, University of York.
    8. Julien Forder & Florin Vadean & Stacey Rand & Juliette Malley, 2018. "The impact of long‐term care on quality of life," Health Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 43-58, March.
    9. Miao Han & Liuquan Sun & Yutao Liu & Jun Zhu, 2018. "Joint analysis of recurrent event data with additive–multiplicative hazards model for the terminal event time," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(5), pages 523-547, July.
    10. Lippi Bruni, Matteo & Ugolini, Cristina & Verzulli, Rossella, 2021. "Should I wait or should I go? Travelling versus waiting for better healthcare," Regional Science and Urban Economics, Elsevier, vol. 89(C).
    11. Paolo Berta & Salvatore Ingrassia & Antonio Punzo & Giorgio Vittadini, 2016. "Multilevel cluster-weighted models for the evaluation of hospitals," METRON, Springer;Sapienza Università di Roma, vol. 74(3), pages 275-292, December.
    12. Sean Shenghsiu Huang & John R. Bowblis, 2019. "Private equity ownership and nursing home quality: an instrumental variables approach," International Journal of Health Economics and Management, Springer, vol. 19(3), pages 273-299, December.
    13. Sankaran, P.G. & Anisha, P., 2012. "Additive hazards models for gap time data with multiple causes," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1454-1462.
    14. Russ Kashian & Nicholas Lovett & Yuhan Xue, 2020. "Has the affordable care act affected health care efficiency?," Journal of Regulatory Economics, Springer, vol. 58(2), pages 193-233, December.
    15. Kwun Chuen Gary Chan & Mei-Cheng Wang, 2017. "Semiparametric Modeling and Estimation of the Terminal Behavior of Recurrent Marker Processes Before Failure Events," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 351-362, January.
    16. Chin-Tsang Chiang & Mei-Cheng Wang, 2009. "Varying-coefficient model for the occurrence rate function of recurrent events," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(1), pages 197-213, March.
    17. Moscelli, G.; & Sayli, M.; & Blanden, J.; & Mello, M.; & Castro-Pires, H.; & Bojke, C.;, 2023. "Non-monetary interventions, workforce retention and hospital quality: evidence from the English NHS," Health, Econometrics and Data Group (HEDG) Working Papers 23/13, HEDG, c/o Department of Economics, University of York.
    18. Paul H. Jensen & Elizabeth Webster & Julia Witt, 2009. "Hospital type and patient outcomes: an empirical examination using AMI readmission and mortality records," Health Economics, John Wiley & Sons, Ltd., vol. 18(12), pages 1440-1460, December.
    19. Dongxiao Han & Xiaogang Su & Liuquan Sun & Zhou Zhang & Lei Liu, 2020. "Variable selection in joint frailty models of recurrent and terminal events," Biometrics, The International Biometric Society, vol. 76(4), pages 1330-1339, December.
    20. Shen, Pao-sheng, 2015. "The inverse probability weighted estimators for distribution functions of the bivariate recurrent events," Statistics & Probability Letters, Elsevier, vol. 106(C), pages 91-99.

    More about this item

    Keywords

    Survival analysis; multistate-model; data simulation; hospital discharge data;
    All these keywords.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bsl:wpaper:2018/14. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: WWZ (email available below). General contact details of provider: https://edirc.repec.org/data/wwzbsch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.