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

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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.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China. [Grant Nos. 71432006, 71471113, 61374095].

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, China

    Xiuxian Wang & Liping Zhou

  2. Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai, China

    Na Geng

  3. Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China

    Jianxin Qiu

  4. Antai College of Economics and Management, Shanghai Jiao Tong University, 1954 Huashan Rd, Shanghai, 20030, People’s Republic of China

    Zhibin Jiang

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  1. Xiuxian Wang
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  2. Na Geng
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  3. Jianxin Qiu
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  4. Zhibin Jiang
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  5. Liping Zhou
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Correspondence to Zhibin Jiang.

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Wang, X., Geng, N., Qiu, J. et al. Markov model and meta-heuristics combined method for cost-effectiveness analysis. Flex Serv Manuf J 32, 213–235 (2020). https://doi.org/10.1007/s10696-019-09369-0

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