Matching-adjusted indirect comparison for survival data analysis: implementation in R language_Chinese Journal of Evidence-Based Medicine

Authors：

HU Hongfei ¹ , MA Yue ¹ , QIU Yijin ¹ , LI Yuxin ¹ ,  ZHOU Ting ^1,2

1. School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing 211198, P. R. China;
2. Center for Pharmacoeconomics and Outcomes Research, China Pharmaceutical University, Nanjing 211198, P. R. China;

Corresponding author：

ZHOU Ting, Email: zhouting20150301@163.com

Keywords：

R language; Matching-adjusted indirect comparisons; Survival data

DOI：

10.7507/1672-2531.202304073

Video：

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Abstract Full text Figures/Tables Video References Cited by

With the increase in the number of single-arm clinical trials and lack of head-to-head clinical studies, the application of unadjusted indirect comparisons and network meta-analysis methods has been limited. Matching-adjusted indirect comparison (MAIC) is an alternative method to fully utilize individual patient data from one study and balance potential bias caused by baseline characteristics differences in different trials through propensity score matching with aggregated data reported in other studies, and complete the comparison of the efficacy between target interventions. This study introduced the concept and principles of MAIC. In addition, we demonstrated how to use the anchored MAIC method based on R language for survival data, which has been widely used in anti-cancer drug evaluation. This study aimed to provide an alternative method to inform evidence-based decisions.

Citation： HU Hongfei, MA Yue, QIU Yijin, LI Yuxin, ZHOU Ting. Matching-adjusted indirect comparison for survival data analysis: implementation in R language. Chinese Journal of Evidence-Based Medicine, 2024, 24(1): 105-111. doi: 10.7507/1672-2531.202304073 Copy

1.	Bucher HC, Guyatt GH, Griffith LE, et al. The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. J Clin Epidemiol, 1997, 50(6): 683-691.
2.	刘凤琪, 石舒原, 杨智荣, 等. 网络Meta分析研究进展系列(七): 多水平网状Meta回归. 中国循证心血管医学杂志, 2020, 12(12): 1409-1412.
3.	Cucherat M, Laporte S, Delaitre O, et al. From single-arm studies to externally controlled studies. Methodological considerations and guidelines. Therapie, 2020, 75(1): 21-27.
4.	Aouni J, Gaudel-Dedieu N, Sebastien B. Matching-adjusted indirect comparisons: application to time-to-event data. Stat Med, 2021, 40(3): 566-577.
5.	Phillippo DM, Dias S, Elsada A, et al. Population adjustment methods for indirect comparisons: a review of National Institute for Health and Care Excellence Technology Appraisals. Int J Technol Assess Health Care, 2019, 35(3): 221-228.
6.	Phillippo DM, Ades AE, Dias S, et al. TSD 18: methods for population-adjusted indirect comparisons in submissions to NICE. 2023.
7.	Phillippo DM, Ades AE, Dias S, et al. Methods for population-adjusted indirect comparisons in health technology appraisal. Med Decis Making, 2018, 38(2): 200-211.
8.	Signorovitch JE, Wu EQ, Yu AP, et al. Comparative effectiveness without head-to-head trials: a method for matching-adjusted indirect comparisons applied to psoriasis treatment with adalimumab or etanercept. Pharmacoeconomics, 2010, 28(10): 935-945.
9.	Austin PC. Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis. Stat Med, 2016, 35(30): 5642-5655.
10.	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. 中国卫生经济, 2020, 39(9): 9-14.
11.	Chen P, Wang X, Zhu S, et al. Economic evaluation of sintilimab plus chemotherapy vs. pembrolizumab plus chemotherapy for the treatment of first-line advanced or metastatic squamous NSCLC. Front Public Health, 2022, 10: 956792.
12.	Maloney DG, Kuruvilla J, Liu FF, et al. Matching-adjusted indirect treatment comparison of liso-cel versus axi-cel in relapsed or refractory large B cell lymphoma. J Hematol Oncol, 2021, 14(1): 140.
13.	Mt-Isa S, Abderhalden LA, Musey L, et al. Matching-adjusted indirect comparison of pneumococcal vaccines V114 and PCV20. Expert Rev Vaccines, 2022, 21(1): 115-123.
14.	Thom H, Cheng V, Keeney E, et al. Matching-adjusted indirect comparison of crisaborole ointment 2% vs. topical calcineurin inhibitors in the treatment of patients with mild-to-moderate atopic dermatitis. Dermatol Ther (Heidelb), 2022, 12(1): 185-194.
15.	刘颖欣, 王若婷, 黎国威. 匹配调整间接比较在医学研究中的应用. 中国循证医学杂志, 2022, 22(10): 1201-1205.
16.	Ishak KJ, Proskorovsky I, Benedict A. Simulation and matching-based approaches for indirect comparison of treatments. Pharmacoeconomics, 2015, 33(6): 537-549.
17.	Veroniki AA, Straus SE, Soobiah C, et al. A scoping review of indirect comparison methods and applications using individual patient data. BMC Med Res Methodol, 2016, 16: 47.
18.	Gregory J, Smith S, Birnie R, et al. MAIC. 2023.

1. Bucher HC, Guyatt GH, Griffith LE, et al. The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. J Clin Epidemiol, 1997, 50(6): 683-691.
2. 刘凤琪, 石舒原, 杨智荣, 等. 网络Meta分析研究进展系列(七): 多水平网状Meta回归. 中国循证心血管医学杂志, 2020, 12(12): 1409-1412.
3. Cucherat M, Laporte S, Delaitre O, et al. From single-arm studies to externally controlled studies. Methodological considerations and guidelines. Therapie, 2020, 75(1): 21-27.
4. Aouni J, Gaudel-Dedieu N, Sebastien B. Matching-adjusted indirect comparisons: application to time-to-event data. Stat Med, 2021, 40(3): 566-577.
5. Phillippo DM, Dias S, Elsada A, et al. Population adjustment methods for indirect comparisons: a review of National Institute for Health and Care Excellence Technology Appraisals. Int J Technol Assess Health Care, 2019, 35(3): 221-228.
6. Phillippo DM, Ades AE, Dias S, et al. TSD 18: methods for population-adjusted indirect comparisons in submissions to NICE. 2023.
7. Phillippo DM, Ades AE, Dias S, et al. Methods for population-adjusted indirect comparisons in health technology appraisal. Med Decis Making, 2018, 38(2): 200-211.
8. Signorovitch JE, Wu EQ, Yu AP, et al. Comparative effectiveness without head-to-head trials: a method for matching-adjusted indirect comparisons applied to psoriasis treatment with adalimumab or etanercept. Pharmacoeconomics, 2010, 28(10): 935-945.
9. Austin PC. Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis. Stat Med, 2016, 35(30): 5642-5655.
10. 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. 中国卫生经济, 2020, 39(9): 9-14.
11. Chen P, Wang X, Zhu S, et al. Economic evaluation of sintilimab plus chemotherapy vs. pembrolizumab plus chemotherapy for the treatment of first-line advanced or metastatic squamous NSCLC. Front Public Health, 2022, 10: 956792.
12. Maloney DG, Kuruvilla J, Liu FF, et al. Matching-adjusted indirect treatment comparison of liso-cel versus axi-cel in relapsed or refractory large B cell lymphoma. J Hematol Oncol, 2021, 14(1): 140.
13. Mt-Isa S, Abderhalden LA, Musey L, et al. Matching-adjusted indirect comparison of pneumococcal vaccines V114 and PCV20. Expert Rev Vaccines, 2022, 21(1): 115-123.
14. Thom H, Cheng V, Keeney E, et al. Matching-adjusted indirect comparison of crisaborole ointment 2% vs. topical calcineurin inhibitors in the treatment of patients with mild-to-moderate atopic dermatitis. Dermatol Ther (Heidelb), 2022, 12(1): 185-194.
15. 刘颖欣, 王若婷, 黎国威. 匹配调整间接比较在医学研究中的应用. 中国循证医学杂志, 2022, 22(10): 1201-1205.
16. Ishak KJ, Proskorovsky I, Benedict A. Simulation and matching-based approaches for indirect comparison of treatments. Pharmacoeconomics, 2015, 33(6): 537-549.
17. Veroniki AA, Straus SE, Soobiah C, et al. A scoping review of indirect comparison methods and applications using individual patient data. BMC Med Res Methodol, 2016, 16: 47.
18. Gregory J, Smith S, Birnie R, et al. MAIC. 2023.

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