IDEAS home Printed from https://ideas.repec.org/a/spr/flsman/v32y2020i1d10.1007_s10696-019-09369-0.html
   My bibliography  Save this article

Markov model and meta-heuristics combined method for cost-effectiveness analysis

Author

Listed:
  • Xiuxian Wang

    (Shanghai Jiao Tong University)

  • Na Geng

    (Shanghai Jiao Tong University)

  • Jianxin Qiu

    (Shanghai Jiao Tong University)

  • Zhibin Jiang

    (Shanghai Jiao Tong University)

  • Liping Zhou

    (Shanghai Jiao Tong University)

Abstract

Cost-effectiveness analysis is an important topic in public health, which can provide valuable information for medical decisions. Several modeling methods are available for conducting cost-effectiveness analysis. However, it is difficult when the data is incomplete. To solve this problem, a Markov model is proposed to model patients’ health states transition, and two hybrid metaheuristics are proposed to estimate the transition probabilities. Based on the estimated transition probabilities, cost-effectiveness analysis is conducted to compare different medical interventions. Numerical experiments and case study validate the effectiveness and practicability of the proposed method. The case study gives the physicians effective instructions by comparing two different immunosuppressants after renal transplantation.

Suggested Citation

  • Xiuxian Wang & Na Geng & Jianxin Qiu & Zhibin Jiang & Liping Zhou, 2020. "Markov model and meta-heuristics combined method for cost-effectiveness analysis," Flexible Services and Manufacturing Journal, Springer, vol. 32(1), pages 213-235, March.
    • Handle: RePEc:spr:flsman:v:32:y:2020:i:1:d:10.1007_s10696-019-09369-0
    DOI: 10.1007/s10696-019-09369-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10696-019-09369-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10696-019-09369-0?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. Tazio Vanni & Jonathan Karnon & Jason Madan & Richard White & W. Edmunds & Anna Foss & Rosa Legood, 2011. "Calibrating Models in Economic Evaluation," PharmacoEconomics, Springer, vol. 29(1), pages 35-49, January.
    2. Jonathan Karnon & James Stahl & Alan Brennan & J. Jaime Caro & Javier Mar & Jörgen Möller, 2012. "Modeling Using Discrete Event Simulation," Medical Decision Making, , vol. 32(5), pages 701-711, September.
    3. Jan Jürgensen & Wolfgang Arns & Bastian Haß, 2010. "Cost-effectiveness of immunosuppressive regimens in renal transplant recipients in Germany: a model approach," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(1), pages 15-25, February.
    4. Douglas Taylor & Vivek Pawar & Denise Kruzikas & Kristen Gilmore & Ankur Pandya & Rowan Iskandar & Milton Weinstein, 2010. "Methods of Model Calibration," PharmacoEconomics, Springer, vol. 28(11), pages 995-1000, November.
    5. Jonathan Karnon & Tazio Vanni, 2011. "Calibrating Models in Economic Evaluation," PharmacoEconomics, Springer, vol. 29(1), pages 51-62, January.
    6. Zvia Agur & Refael Hassin & Sigal Levy, 2006. "Optimizing Chemotherapy Scheduling Using Local Search Heuristics," Operations Research, INFORMS, vol. 54(5), pages 829-846, October.
    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. Paola Cappanera & Jingshan Li & Evren Sahin & Nico J. Vandaele & Filippo Visintin, 2020. "Editorial for the special issue on “Modelling, simulation, and optimization in health care”," Flexible Services and Manufacturing Journal, Springer, vol. 32(1), pages 1-5, March.

    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. Douglas Taylor & Vivek Pawar & Denise Kruzikas & Kristen Gilmore & Myrlene Sanon & Milton Weinstein, 2012. "Incorporating Calibrated Model Parameters into Sensitivity Analyses," PharmacoEconomics, Springer, vol. 30(2), pages 119-126, February.
    2. C Marijn Hazelbag & Jonathan Dushoff & Emanuel M Dominic & Zinhle E Mthombothi & Wim Delva, 2020. "Calibration of individual-based models to epidemiological data: A systematic review," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-17, May.
    3. Meimei Wang & Steffen Flessa, 2020. "Modelling Covid-19 under uncertainty: what can we expect?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(5), pages 665-668, July.
    4. Jon Duan & G. Cornelis van Kooten & A. T. M. Hasibul Islam, 2023. "Calibration of Grid Models for Analyzing Energy Policies," Energies, MDPI, vol. 16(3), pages 1-21, January.
    5. Jing Voon Chen & Julia L. Higle & Michael Hintlian, 2018. "A systematic approach for examining the impact of calibration uncertainty in disease modeling," Computational Management Science, Springer, vol. 15(3), pages 541-561, October.
    6. Steffen Flessa & Dominik Dietz & Elisabete Weiderpass, 2016. "Health policy support under extreme uncertainty: the case of cervical cancer in Cambodia," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 4(3), pages 183-218, November.
    7. Candio, Paolo & Meads, David & Hill, Andrew J. & Bojke, Laura, 2020. "Modelling the impact of physical activity on public health: A review and critique," Health Policy, Elsevier, vol. 124(10), pages 1155-1164.
    8. Penny R. Breeze & Hazel Squires & Kate Ennis & Petra Meier & Kate Hayes & Nik Lomax & Alan Shiell & Frank Kee & Frank de Vocht & Martin O’Flaherty & Nigel Gilbert & Robin Purshouse & Stewart Robinson , 2023. "Guidance on the use of complex systems models for economic evaluations of public health interventions," Health Economics, John Wiley & Sons, Ltd., vol. 32(7), pages 1603-1625, July.
    9. Natalie Kronik & Yuri Kogan & Moran Elishmereni & Karin Halevi-Tobias & Stanimir Vuk-Pavlović & Zvia Agur, 2010. "Predicting Outcomes of Prostate Cancer Immunotherapy by Personalized Mathematical Models," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-8, December.
    10. Louise Freebairn & Jo-An Atkinson & Nathaniel D Osgood & Paul M Kelly & Geoff McDonnell & Lucie Rychetnik, 2019. "Turning conceptual systems maps into dynamic simulation models: An Australian case study for diabetes in pregnancy," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-27, June.
    11. Stefan Scholz & Thomas Mittendorf, 2014. "Modeling rheumatoid arthritis using different techniques - a review of model construction and results," Health Economics Review, Springer, vol. 4(1), pages 1-16, December.
    12. Miguel Ortiz-Barrios & Juan-José Alfaro-Saiz, 2020. "An integrated approach for designing in-time and economically sustainable emergency care networks: A case study in the public sector," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.
    13. Matthew R Behrend & María-Gloria Basáñez & Jonathan I D Hamley & Travis C Porco & Wilma A Stolk & Martin Walker & Sake J de Vlas & for the NTD Modelling Consortium, 2020. "Modelling for policy: The five principles of the Neglected Tropical Diseases Modelling Consortium," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(4), pages 1-17, April.
    14. Simone A. Huygens & Isaac Corro Ramos & Carlijn V. C. Bouten & Jolanda Kluin & Shih Ting Chiu & Gary L. Grunkemeier & Johanna J. M. Takkenberg & Maureen P. M. H. Rutten-van Mölken, 2020. "Early cost-utility analysis of tissue-engineered heart valves compared to bioprostheses in the aortic position in elderly patients," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(4), pages 557-572, June.
    15. David Byrd & Sruthi Palaparthi & Maria Hybinette & Tucker Hybinette Balch, 2020. "The Importance of Low Latency to Order Book Imbalance Trading Strategies," Papers 2006.08682, arXiv.org.
    16. Özge Karanfil & Yaman Barlas, 2008. "A Dynamic Simulator for the Management of Disorders of the Body Water Homeostasis," Operations Research, INFORMS, vol. 56(6), pages 1474-1492, December.
    17. David Epstein & Leticia García-Mochón & Stephen Kaptoge & Simon G. Thompson, 2016. "Modeling the costs and long-term health benefits of screening the general population for risks of cardiovascular disease: a review of methods used in the literature," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(8), pages 1041-1053, November.
    18. Sera Kahruman & Elif Ulusal & Sergiy Butenko & Illya Hicks & Kathleen Diehl, 2012. "Scheduling the adjuvant endocrine therapy for early stage breast cancer," Annals of Operations Research, Springer, vol. 196(1), pages 683-705, July.
    19. Victoria A Wade & Jonathan Karnon & Jaklin A Eliott & Janet E Hiller, 2012. "Home Videophones Improve Direct Observation in Tuberculosis Treatment: A Mixed Methods Evaluation," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-13, November.
    20. Jinghua Shi & Oguzhan Alagoz & Fatih Erenay & Qiang Su, 2014. "A survey of optimization models on cancer chemotherapy treatment planning," Annals of Operations Research, Springer, vol. 221(1), pages 331-356, 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:flsman:v:32:y:2020:i:1:d:10.1007_s10696-019-09369-0. 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.