The partitioned survival model (PSM), utilized in pharmacoeconomics (PE) evaluation for decision- making models, is widely applied in PE evaluation of international oncology treatments due to its simplicity, intuitiveness, and direct data analysis from clinical literature, circumventing complex inter-state transition probability calculations. The R language, known for its efficiency, intuitive nature, and reproducibility of results, has been equipped with various specialized packages through years of application by researchers in Europe and America, significantly enhancing research productivity. However, there were few studies about  the use of R language for PSM analysis of oncology drug treatments in China. This article aims to present a comprehensive operational procedure for PSM analysis of oncology drug treatments using R language, combined with practical cases, and to offer a reference for researchers in the field.

1.Woods BS, Sideris E, Palmer SJ, et al. NICE DSU technical support document 19: partitioned survival analysis for decision  modelling in health care: a critical review[EB/ OL]. (2017-06-02). https://www.sheffield.ac.uk/sites/default/files/2022-02/TSD19-Partitioned-Survival-Analysis-final-report.pdf

2.Bullement A, Cranmer HL, Shields GE. A review of recent decision-analytic models used to evaluate the economic value of cancer treatments[J]. Appl Health Econ Health Policy, 2019, 17(6): 771-780. DOI: 10.1007/s40258-019-00513-3.

3.孟蕊, 芮明军, 王欣恬, 等. 不同决策分析模型在抗肿瘤药物经济学评价中的应用——以加拿大CADTH为例[J]. 中国药房, 2021, 32(14): 1752-1757. [Meng R, Rui MJ, Wang XT, et al. Application of different decision analysis models in economic evaluation of antitumor drugs: taking CADTH in Canada as an example[J]. Chinese Pharmacy, 2021, 32(14): 1752-1757.] DOI: 10.6039/j.issn.1001-0408.2021.14.15.

4.Woods BS, Sideris E, Palmer S, et al. Partitioned survival and state transition models for healthcare decision making in oncology: where are we now?[J]. Value Health, 2020, 23(12): 1613-1621. DOI: 10.1016/j.jval.2020.08.2094.

5.Green N, Lamrock F, Naylor N, et al. Health economic evaluation using Markov models in R for Microsoft Excel users: a tutorial[J]. Pharmacoeconomics, 2023, 41(1): 5-19. DOI: 10.1007/s40273-022-01199-7.

6.邵荣杰, 唐文熙, 马爱霞. 分区生存模型在药物经济学评价中的应用[J]. 中国卫生经济, 2019, 38(9): 60-63. [Shao RJ, Tang WX, Ma AX. The partitioned survival model applied in pharmacoeconomic evaluation[J]. Chinese Health Economics, 2019, 38(9): 60-63.] DOI: 10.7664/CHE20190916.

7.曾小慧, 彭六保, 谭重庆, 等. 药物经济学评价中的分区生存模型[J]. 中国新药与临床杂志, 2020, 39(8): 504-507. [Zeng XH, Peng LB, Tan CQ, et al. Partitioned survival model in pharmacoeconomics[J]. Chinese Journal of New Drugs and Clinical Remedies, 2020, 39(8): 504-507.] DOI: 10.14109/j.cnki.xyylc.2020.08.12.

8.刘新义, 谭重庆, 曾小慧, 等. 分区生存模型在药物经济学评价中的应用简介及实例解析[J]. 中国现代应用药学, 2019, 36(24): 3090-3093. [Liu XY, Tan CQ, Zeng XH, et al. Introduction of partitioned survival model in pharmacoeconomics evaluation and case analysis[J]. Chinese Journal of Modern Applied Pharmacy, 2019, 36(24): 3090-3093.] DOI: 10.13748/j.cnki.issn1007-7693.2019.24.018.

9.桂裕亮, 韩晟, 翁鸿, 等. 应用TreeAge Pro软件实现基于Markov模型的成本-效果分析[J]. 中国循证医学杂志, 2018, 18(1): 116-120. [Gui YL, Han S, Weng H, et al. Cost-effectiveness analysis based on Markov model using TreeAge Pro software[J]. Chinese Journal of Evidence-Based Medicine, 2018, 18(1): 116-120.] DOI: 10.7507/1672-2531.201707008.

10.Filipović-Pierucci A, Zarca K, Durand-Zaleski I. Markov models for health economic evaluations in R with the heemod package[J]. Value Health, 2016, 19(7): 369. DOI: 10.1016/j.jval.2016.09.133.

11.母立峰, 苏亚霞, 宋金春. 基于R语言的药物经济学研究方法综述[J]. 药物流行病学杂志, 2019, 28(7): 472-476. [Mu LF, Su YX, Song JC. A review of the research method of pharmacoeconomics based on R language[J]. Chinese Journal of Pharmacoepidemiology, 2019, 28(7): 472-476.] DOI: 10.19960/j.cnki.issn1005-0698.2019.07.013.

12.Jackson CH. flexsurv: a platform for parametric survival modeling in R[J]. J Stat Softw, 2016, 70: i08. DOI: 10.18637/jss.v070.i08.

13.Incerti D, Jansen JP. hesim: health economic simulation modeling and decision analysis[EB/OL]. (2021-03-09) [2023-06-30]. https://doi.org/10.48550/arXiv.2102.09437.

14.Moore DF. Applied survival analysis using R[M]. Cham: Springer, 2016.

15.Baio G. survHE: survival analysis for health economic evaluation and cost-effectiveness modeling[J]. J Stat Softw, 2020, 95(14): 1-47. DOI: 10.18637/jss.v095.i14.

16.石丰豪, 商叶, 芮明军, 等. R语言survHE程序包在卫生经济学评价中的应用[J]. 中国卫生经济, 2020, 39(9): 9-14. [Shi FH, Shang Y, Rui MJ, et al. Application of survHE package of R for health economic evaluation[J]. China Health Economics, 2020, 39(9): 9-14.] DOI: 10.7664/CHE20200902.

17.Baio G, Berardi A, Heath A. Bayesian cost-effectiveness analysis with the R package BCEA[M]. New York: Springer, 2017.

18.Ben ÂJ, van Dongen JM, El Alili M, et al. Conducting trial-based economic evaluations using R: a tutorial[J]. Pharmacoeconomics. 2023, 41(11): 1403-1413. DOI: 10.1007/s40273-023-01301-7.

19.Rodríguez-Abreu D, Powell SF, Hochmair MJ, et al. Pemetrexed plus platinum with or without pembrolizumab in patients with previously untreated metastatic nonsquamous NSCLC: protocol-specified final analysis from KEYNOTE-189[J]. An Oncol, 2021, 32(7): 881-895. DOI: 10.1016/j.annonc.2021.04.008.

20.Gadgeel S, Rodríguez-Abreu D, Speranza G, et al. Updated analysis from KEYNOTE-189: pembrolizumab or placebo plus pemetrexed and platinum for previously untreated metastatic nonsquamous non-small-cell lung cancer[J]. J Clin Oncol, 2020, 38(14): 1505-1517. DOI: 10.1200/JCO.19.03136.

21.徐赫, 马爱霞. 基于分区生存模型的帕博利珠单抗单药与化疗一线治疗PD-L1肿瘤比例分数不同的非小细胞肺癌的成本-效果分析[J]. 中国医院药学杂志, 2020, 40(23): 2468-2473. [Xu H, Ma AX. Cost-effectiveness analysis of pembrolizumab versus chemotherapy as first-line treatment in non-small cell lung cancer with different PD-L1 expression levels based on partitioned survival model[J]. Chinese Journal of Hospital Pharmacy, 2020, 40(23): 2468-2473.] DOI: 10.13286/j.1001-5213.2020.23.14.