IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v216y2014i1p101-12810.1007-s10479-013-1359-7.html
   My bibliography  Save this article

Outcomes matter: estimating pre-transplant survival rates of kidney-transplant patients using simulator-based propensity scores

Author

Listed:
  • Inbal Yahav
  • Galit Shmueli

Abstract

The current kidney allocation system in the United States fails to match donors and recipients well. In an effort to improve the allocation system, the United Network of Organ Sharing (UNOS) defined factors that should determine a new allocation policy, and particularly patients’ potential remaining lifetime without a transplant (pre-transplant survival rates). Estimating pre-transplant survival rates is challenging because the data available on candidates and organ recipients is already “contaminated” by the current allocation policy. In particular, the selection of patients who are offered (and decide to accept) a kidney is not random. We therefore expect differences in mortality-related characteristics of organ recipients and of candidates who have not received transplant. Such differences introduce bias into survival models. Existing approaches for tackling this selection bias either ignore the difference between candidates and recipients or assume that selection to transplant is based solely on patients’ pre-transplant information, irrespective of the potential allocation outcome. We argue that in practice the allocation is dependent on the anticipated allocation outcome, which is a major factor in selection to transplant. Moreover, we show that ignoring the anticipated outcome increases model bias rather than decreases it. In this paper, we propose a novel simulator-based approach (SimBa) that adjusts for the selection bias by taking into account both pre-transplant and anticipated outcome information using simulation. We then fit survival models to kidney transplant waitlist data and compare the different adjustment methods. We find that SimBa not only fits the data best, but also captures a key aspect of the current allocation policy, namely, that the probability of kidney allocation increases in the expected pre-transplant life years. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Inbal Yahav & Galit Shmueli, 2014. "Outcomes matter: estimating pre-transplant survival rates of kidney-transplant patients using simulator-based propensity scores," Annals of Operations Research, Springer, vol. 216(1), pages 101-128, May.
    • Handle: RePEc:spr:annopr:v:216:y:2014:i:1:p:101-128:10.1007/s10479-013-1359-7
    DOI: 10.1007/s10479-013-1359-7
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-013-1359-7
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10479-013-1359-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ron S. Kenett & Galit Shmueli, 2014. "On information quality," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(1), pages 3-38, January.
    2. H.‐-J Shyur & E.A. Elsayed & J.T. Luxhøj, 1999. "A general hazard regression modelfor accelerated life testing," Annals of Operations Research, Springer, vol. 91(0), pages 263-280, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tomasz Hachaj & Marek R. Ogiela & Katarzyna Koptyra, 2018. "Human actions recognition from motion capture recordings using signal resampling and pattern recognition methods," Annals of Operations Research, Springer, vol. 265(2), pages 223-239, June.

    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. Pierpaolo D’Urso & Vincenzina Vitale, 2020. "Bayesian Networks Model Averaging for Bes Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 151(3), pages 897-919, October.
    2. Pierpaolo D’Urso & Vincenzina Vitale, 2021. "Modeling Local BES Indicators by Copula-Based Bayesian Networks," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(3), pages 823-847, February.
    3. Federica Cugnata & Silvia Salini, 2014. "Model-based approach for importance–performance analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3053-3064, November.
    4. Coleman Shirley Y., 2016. "Data-Mining Opportunities for Small and Medium Enterprises with Official Statistics in the UK," Journal of Official Statistics, Sciendo, vol. 32(4), pages 849-865, December.
    5. Domenico Piccolo & Rosaria Simone, 2019. "Rejoinder to the discussion of “The class of cub models: statistical foundations, inferential issues and empirical evidence”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 477-493, September.
    6. Galit Shmueli, 2020. "Discussion on “Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer‐Lemeshow test” by Giovanni Nattino, Michael L. Pennell, and Stanley Lemeshow," Biometrics, The International Biometric Society, vol. 76(2), pages 561-563, June.
    7. Paola Zola & Paulo Cortez & Costantino Ragno & Eugenio Brentari, 2019. "Social Media Cross-Source and Cross-Domain Sentiment Classification," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1469-1499, September.
    8. Moustafa, Kassem & Hu, Zhen & Mourelatos, Zissimos P. & Baseski, Igor & Majcher, Monica, 2021. "System reliability analysis using component-level and system-level accelerated life testing," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    9. Biemer Paul & Trewin Dennis & Bergdahl Heather & Japec Lilli, 2014. "A System for Managing the Quality of Official Statistics," Journal of Official Statistics, Sciendo, vol. 30(3), pages 1-35, September.
    10. Kenett Ron S. & Shmueli Galit, 2016. "From Quality to Information Quality in Official Statistics," Journal of Official Statistics, Sciendo, vol. 32(4), pages 867-885, December.
    11. Ruojing Zhang & Marta Indulska & Shazia Sadiq, 2019. "Discovering Data Quality Problems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(5), pages 575-593, October.
    12. Ron S. Kenett & Abraham Rubinstein, 2021. "Generalizing research findings for enhanced reproducibility: an approach based on verbal alternative representations," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4137-4151, May.
    13. Rosaria Simone, 2023. "Uncertainty Diagnostics of Binomial Regression Trees for Ordered Rating Data," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 79-105, April.
    14. Mahsa Ashouri & Kate Cai & Furen Lin & Galit Shmueli, 2018. "Assessing the Value of an Information System for Developing Predictive Analytics: The Case of Forecasting School-Level Demand in Taiwan," Service Science, INFORMS, vol. 10(1), pages 58-75, March.
    15. Nikolaos Askitas, 2016. "Big Data is a big deal but how much data do we need? [Big Data gut und schön. Aber wie viel Data brauchen wir?]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(2), pages 113-125, October.

    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:spr:annopr:v:216:y:2014:i:1:p:101-128:10.1007/s10479-013-1359-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.