Introduction and application example of unanchored matching-adjusted indirect comparison method_Chinese Journal of Evidence-Based Medicine

Authors：

LIU Ying , HE Xiaoning , LIU Jia ,  WU Jing

School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, P. R. China;

Corresponding author：

WU Jing, Email: jingwu@tju.edu.cn

Keywords：

Matching-adjusted indirect comparison; Individual patient data; Treatment effects; Clinical trials

DOI：

10.7507/1672-2531.202303105

Video：

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When there is a lack of head-to-head randomized controlled trials between two interventions of interest, indirect comparison methods can be employed to estimate their relative treatment effects. Matching-adjusted indirect comparison (MAIC) is a population-adjusted indirect comparison method that utilizes a weighting approach. Unanchored MAIC is particularly applicable in scenarios where a common control group between the two interventions is not available. This article introduces the background and mathematical theory of unanchored MAIC, along with a demonstration of the operational steps and interpretation of results through an application example.

Citation： LIU Ying, HE Xiaoning, LIU Jia, WU Jing. Introduction and application example of unanchored matching-adjusted indirect comparison method. Chinese Journal of Evidence-Based Medicine, 2023, 23(9): 1096-1101. doi: 10.7507/1672-2531.202303105 Copy

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12.	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.
13.	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.
14.	Hatswell AJ, Freemantle N, Baio G. The effects of model misspecification in unanchored matching-adjusted indirect comparison: results of a simulation study. Value Health, 2020, 23(6): 751-759.
15.	Jiang Y, Ni W. Performance of unanchored matching-adjusted indirect comparison (MAIC) for the evidence synthesis of single-arm trials with time-to-event outcomes. BMC Med Res Methodol, 2020, 20(1): 241.
16.	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.
17.	Jansen JP. Network meta-analysis of individual and aggregate level data. Res Synth Methods, 2012, 3(2): 177-190.
18.	Belger MBA, Kadziola Z, Petto H, et al. Alternative weighting approaches for matching adjusted indirect comparisons (MAIC). Philadelphia: ISPOR 20th Annual International Meeting, 2015.
19.	Jackson D, Rhodes K, Ouwens M. Alternative weighting schemes when performing matching-adjusted indirect comparisons. Res Synth Methods, 2021, 12(3): 333-346.
20.	Weber D, Jensen K, Kieser M. Comparison of methods for estimating therapy effects by indirect comparisons: a simulation study. Med Decis Making, 2020, 40(5): 644-654.
21.	Remiro-Azócar A, Heath A, Baio G. Methods for population adjustment with limited access to individual patient data: a review and simulation study. Res Synth Methods, 2021, 12(6): 750-775.
22.	Caldwell DM, Welton NJ, Dias S, et al. Selecting the best scale for measuring treatment effect in a network meta-analysis: a case study in childhood nocturnal enuresis. Res Synth Methods, 2012, 3(2): 126-141.
23.	Banack HR, Hayes-Larson E, Mayeda ER. Monte carlo simulation approaches for quantitative bias analysis: a tutorial. Epidemiol Rev, 2022, 43(1): 106-117.
24.	Burton A, Altman DG, Royston P, et al. The design of simulation studies in medical statistics. Stat Med, 2006, 25(24): 4279-4292.
25.	Ahrens JH, Dieter U. Computer methods for sampling from the exponential and normal distributions. Commun ACM, 1972, 15: 873-882.
26.	Bender R, Augustin T, Blettner M. Generating survival times to simulate Cox proportional hazards models. Stat Med, 2005, 24(11): 1713-1723.

1. Hariton E, Locascio JJ. Randomised controlled trials - the gold standard for effectiveness research: study design: randomised controlled trials. BJOG, 2018, 125(13): 1716.
2. Nambudiri VE, Qureshi A. Comparative effectiveness research. J Invest Dermatol, 2013, 133(2): e5.
3. 胡晶, 杨智荣, 詹思延. 间接比较和多种干预措施比较的方法学及应用. 中国循证儿科杂志, 2011, 6(6): 456-459.
4. 田金徽, Song F, 李伦, 等. 间接比较方法简介. 中国循证医学杂志, 2014, 14(3): 365-368.
5. Zwahlen M, Salanti G. Causal inference from experiment and observation. Evid Based Ment Health, 2018, 21(1): 34-38.
6. Song F, Altman DG, Glenny AM, et al. Validity of indirect comparison for estimating efficacy of competing interventions: empirical evidence from published meta-analyses. BMJ, 2003, 326(7387): 472.
7. Jansen JP, Fleurence R, Devine B, et al. Interpreting indirect treatment comparisons and network meta-analysis for health-care decision making: report of the ISPOR task force on indirect treatment comparisons good research practices: part 1. Value Health, 2011, 14(4): 417-428.
8. Phillippo DAT, Dias S, Palmer S, et al. NICE DSU technical support document 18: methods for population-adjusted indirect comparisons in submissions to NICE. Sheffield: University of Sheffield, NICE Decision Support Unit, 2016.
9. Hernán MA, Hernández-Díaz S. Beyond the intention-to-treat in comparative effectiveness research. Clin Trials, 2012, 9(1): 48-55.
10. Signorovitch JE, Sikirica V, Erder MH, et al. Matching-adjusted indirect comparisons: a new tool for timely comparative effectiveness research. Value Health, 2012, 15(6): 940-947.
11. 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.
12. 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.
13. 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.
14. Hatswell AJ, Freemantle N, Baio G. The effects of model misspecification in unanchored matching-adjusted indirect comparison: results of a simulation study. Value Health, 2020, 23(6): 751-759.
15. Jiang Y, Ni W. Performance of unanchored matching-adjusted indirect comparison (MAIC) for the evidence synthesis of single-arm trials with time-to-event outcomes. BMC Med Res Methodol, 2020, 20(1): 241.
16. 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.
17. Jansen JP. Network meta-analysis of individual and aggregate level data. Res Synth Methods, 2012, 3(2): 177-190.
18. Belger MBA, Kadziola Z, Petto H, et al. Alternative weighting approaches for matching adjusted indirect comparisons (MAIC). Philadelphia: ISPOR 20th Annual International Meeting, 2015.
19. Jackson D, Rhodes K, Ouwens M. Alternative weighting schemes when performing matching-adjusted indirect comparisons. Res Synth Methods, 2021, 12(3): 333-346.
20. Weber D, Jensen K, Kieser M. Comparison of methods for estimating therapy effects by indirect comparisons: a simulation study. Med Decis Making, 2020, 40(5): 644-654.
21. Remiro-Azócar A, Heath A, Baio G. Methods for population adjustment with limited access to individual patient data: a review and simulation study. Res Synth Methods, 2021, 12(6): 750-775.
22. Caldwell DM, Welton NJ, Dias S, et al. Selecting the best scale for measuring treatment effect in a network meta-analysis: a case study in childhood nocturnal enuresis. Res Synth Methods, 2012, 3(2): 126-141.
23. Banack HR, Hayes-Larson E, Mayeda ER. Monte carlo simulation approaches for quantitative bias analysis: a tutorial. Epidemiol Rev, 2022, 43(1): 106-117.
24. Burton A, Altman DG, Royston P, et al. The design of simulation studies in medical statistics. Stat Med, 2006, 25(24): 4279-4292.
25. Ahrens JH, Dieter U. Computer methods for sampling from the exponential and normal distributions. Commun ACM, 1972, 15: 873-882.
26. Bender R, Augustin T, Blettner M. Generating survival times to simulate Cox proportional hazards models. Stat Med, 2005, 24(11): 1713-1723.

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