IDEAS home Printed from https://ideas.repec.org/p/ecl/stabus/3283.html
   My bibliography  Save this paper

Data Uncertainty in Markov Chains: Application to Cost-Effectiveness Analyses of Medical Innovations

Author

Listed:
  • Goh, Joel

    (Stanford University)

  • Bayati, Mohsen

    (Stanford University)

  • Zenios, Stefanos A.

    (Stanford University)

  • Singh, Sundeep

    (Stanford University)

  • Moore, David

    (Stanford University)

Abstract

Cost-effectiveness studies of medical innovations often suffer from data inadequacy. When Markov chains are used as a modeling framework for such studies, this data inadequacy can manifest itself as imprecise estimates for many elements of the transition matrix. In this paper, we study how to compute maximal and minimal values for the discounted value of the chain (with respect to a vector of state-wise costs or rewards) as these uncertain transition parameters jointly vary within a given uncertainty set. We show that these problems are computationally tractable if the uncertainty set has a row-wise structure. Conversely, we prove that if the row-wise structure is relaxed slightly, the problems become computationally intractable (NP-hard). We apply our model to assess the cost-effectiveness of fecal immunochemical testing (FIT), a new screening method for colorectal cancer. Our results show that despite the large uncertainty in FIT's performance, it is highly cost-effective relative to the prevailing screening method of colonoscopy.

Suggested Citation

  • Goh, Joel & Bayati, Mohsen & Zenios, Stefanos A. & Singh, Sundeep & Moore, David, 2015. "Data Uncertainty in Markov Chains: Application to Cost-Effectiveness Analyses of Medical Innovations," Research Papers 3283, Stanford University, Graduate School of Business.
    • Handle: RePEc:ecl:stabus:3283
    as

    Download full text from publisher

    File URL: http://www.gsb.stanford.edu/faculty-research/working-papers/data-uncertainty-markov-chains-application-cost-effectiveness
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Boloori, Alireza & Saghafian, Soroush & Chakkera, Harini A. A. & Cook, Curtiss B., 2017. "Data-Driven Management of Post-transplant Medications: An APOMDP Approach," Working Paper Series rwp17-036, Harvard University, John F. Kennedy School of Government.
    2. Yuanhui Zhang & Haipeng Wu & Brian T. Denton & James R. Wilson & Jennifer M. Lobo, 2019. "Probabilistic sensitivity analysis on Markov models with uncertain transition probabilities: an application in evaluating treatment decisions for type 2 diabetes," Health Care Management Science, Springer, vol. 22(1), pages 34-52, March.

    More about this item

    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:ecl:stabus:3283. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/gsstaus.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.