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. Jonathan Karnon & Tazio Vanni, 2011. "Calibrating Models in Economic Evaluation," PharmacoEconomics, Springer, vol. 29(1), pages 51-62, January.
    2. Zvia Agur & Refael Hassin & Sigal Levy, 2006. "Optimizing Chemotherapy Scheduling Using Local Search Heuristics," Operations Research, INFORMS, vol. 54(5), pages 829-846, October.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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. 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.
    2. 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.
    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. 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.
    6. 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.
    7. 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.
    8. 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.
    9. repec:plo:pone00:0083389 is not listed on IDEAS
    10. Robert Saltzman & Theresa Roeder & Judith Lambton & Lila Param & Brian Frost & Roxanne Fernandes, 2017. "The Impact of a Discharge Holding Area on the Throughput of a Pediatric Unit," Service Science, INFORMS, vol. 9(2), pages 121-135, June.
    11. 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.
    12. repec:plo:pone00:0097459 is not listed on IDEAS
    13. 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.
    14. 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.
    15. 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.
    16. Jan Jürgensen & Robert Ikenberg & Roger-Axel Greiner & Volker Hösel, 2015. "Cost-effectiveness of modern mTOR inhibitor based immunosuppression compared to the standard of care after renal transplantation in Germany," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(4), pages 377-390, May.
    17. Vojtěch Kamenský & Vladimír Rogalewicz & Ondřej Gajdoš & Gleb Donin, 2022. "Discrete Event Simulation Model for Cost-Effectiveness Evaluation of Screening for Asymptomatic Patients with Lower Extremity Arterial Disease," IJERPH, MDPI, vol. 19(18), pages 1-16, September.
    18. Borja García-Lorenzo & Ania Gorostiza & Nerea González & Igor Larrañaga & Maider Mateo-Abad & Ana Ortega-Gil & Janika Bloemeke & Oliver Groene & Itziar Vergara & Javier Mar & Sarah N. Lim Choi Keung &, 2023. "Assessment of the Effectiveness, Socio-Economic Impact and Implementation of a Digital Solution for Patients with Advanced Chronic Diseases: The ADLIFE Study Protocol," IJERPH, MDPI, vol. 20(4), pages 1-14, February.
    19. repec:plo:pone00:0218875 is not listed on IDEAS
    20. Lionel Perrier & Pauline Rascle & Magali Morelle & Maud Toulmonde & Dominique Ranchere Vince & Axel Le Cesne & Philippe Terrier & Agnès Neuville & Pierre Méeus & Fadila Farsi & Françoise Ducimetière &, 2018. "The cost-saving effect of centralized histological reviews with soft tissue and visceral sarcomas, GIST, and desmoid tumors: The experiences of the pathologists of the French Sarcoma Group," Post-Print halshs-01784301, HAL.
    21. 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.
    22. Alice Rondini & Fabiana Tornese & Maria Grazia Gnoni & Giuditta Pezzotta & Roberto Pinto, 2017. "Hybrid simulation modelling as a supporting tool for sustainable product service systems: a critical analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 55(23), pages 6932-6945, December.
    23. 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.

    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.