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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
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    References listed on IDEAS

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    1. 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.
    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. Jonathan Karnon & Tazio Vanni, 2011. "Calibrating Models in Economic Evaluation," PharmacoEconomics, Springer, vol. 29(1), pages 51-62, January.
    4. Zvia Agur & Refael Hassin & Sigal Levy, 2006. "Optimizing Chemotherapy Scheduling Using Local Search Heuristics," Operations Research, INFORMS, vol. 54(5), pages 829-846, October.
    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.
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    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.

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