Introduction of matching-adjusted indirect comparison in medical research_Chinese Journal of Evidence-Based Medicine

Authors：

LIU Yingxin , WANG Ruoting ,  LI Guowei

Center for Clinical Epidemiology and Methodology (CCEM), Guangdong Second Provincial General Hospital, Guangzhou 510317, P. R. China;

Corresponding author：

LI Guowei, Email: ligw@gd2h.org.cn

Keywords：

Matching-adjusted indirect comparison; Indirect comparison; Clinical trial; Randomized controlled trial

DOI：

10.7507/1672-2531.202204127

Video：

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Abstract Full text Figures/Tables Video References Cited by

Randomized controlled trials (RCTs) are currently the gold standard for the treatment effect comparisons; however, it is sometimes not feasible to conduct an RCT due to ethical and economic reasons. In the absence of evidence for head-to-head RCT direct comparison, the indirect comparison technique is an effective and resource-saving alternative. Matching-adjusted indirect comparison (MAIC) is an attractive method in the field of population-adjusted indirect comparisons between two trials. It can adjust for between-trial imbalances in the distribution of observed covariates by weighting the available individual patient data of the studied intervention and then match the aggregated data of the controlled intervention. Subsequently, the treatment effect comparison can be evaluated through the post-matched population. Although MAIC is gaining increasing attention in clinical research, especially in the evaluation of new drugs, efforts are still largely required for knowledge dissemination in China. In this paper, we briefly introduced the concepts, research value and examples, and pros and cons of MAIC.

Citation： LIU Yingxin, WANG Ruoting, LI Guowei. Introduction of matching-adjusted indirect comparison in medical research. Chinese Journal of Evidence-Based Medicine, 2022, 22(10): 1201-1205. doi: 10.7507/1672-2531.202204127 Copy

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2.	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.
3.	Signorovitch JE, Wu EQ, Betts KA, et al. Comparative efficacy of nilotinib and dasatinib in newly diagnosed chronic myeloid leukemia: a matching-adjusted indirect comparison of randomized trials. Curr Med Res Opin, 2011, 27(6): 1263-1271.
4.	Garcia-Foncillas J, Bokemeyer C, Italiano A, et al. Indirect treatment comparison of larotrectinib versus entrectinib in treating patients with TRK gene fusion cancers. Cancers (Basel), 2022, 14(7): 1793.
5.	Phillippo D, Ades T, Dias 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.
6.	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.
7.	Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika, 1983, 70(1): 41-55.
8.	Austin PC. The performance of different propensity score methods for estimating marginal odds ratios. Stat Med, 2007, 26(16): 3078-3094.
9.	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.
10.	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.
11.	Song F, Loke YK, Walsh T, et al. Methodological problems in the use of indirect comparisons for evaluating healthcare interventions: survey of published systematic reviews. BMJ, 2009, 338: b1147.
12.	Berlie HD, Kalus JS, Jaber LA. Thiazolidinediones and the risk of edema: a meta-analysis. Diabetes Res Clin Pract, 2007, 76(2): 279-289.
13.	Boonen S, Lips P, Bouillon R, et al. Need for additional calcium to reduce the risk of hip fracture with vitamin d supplementation: evidence from a comparative metaanalysis of randomized controlled trials. J Clin Endocrinol Metab, 2007, 92(4): 1415-1423.
14.	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.
15.	Lumley T. Network meta-analysis for indirect treatment comparisons. Stat Med, 2002, 21(16): 2313-2324.
16.	Flury BK, Riedwyl H. Standard distance in univariate and multivariate analysis. Am Stat, 1986, 40(3): 249-251.
17.	Austin PC, Grootendorst P, Anderson GM. A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study. Stat Med, 2007, 26(4): 734-753.
18.	Halmos B, Burke T, Kalyvas C, et al. Pembrolizumab + chemotherapy versus atezolizumab + chemotherapy+/-bevacizumab for the first-line treatment of non-squamous NSCLC: a matching-adjusted indirect comparison. Lung Cancer, 2021, 155: 175-182.
19.	Awad MM, Gadgeel SM, Borghaei H, et al. Long-term overall survival from KEYNOTE-021 Cohort G: pemetrexed and carboplatin with or without pembrolizumab as first-line therapy for advanced nonsquamous NSCLC. J Thorac Oncol, 2021, 16(1): 162-168.
20.	Gandhi L, Rodríguez-Abreu D, Gadgeel S, et al. Pembrolizumab plus chemotherapy in metastatic non-small-cell lung cancer. N Engl J Med, 2018, 378(22): 2078-2092.
21.	West H, McCleod M, Hussein M, et al. Atezolizumab in combination with carboplatin plus nab-paclitaxel chemotherapy compared with chemotherapy alone as first-line treatment for metastatic non-squamous non-small-cell lung cancer (IMpower130): a multicentre, randomised, open-label, phase 3 trial. Lancet Oncol, 2019, 20(7): 924-937.
22.	Guyot P, Ades AE, Ouwens MJ, et al. Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Med Res Methodol, 2012, 12: 9.
23.	Halmos B, Burke T, Kalyvas C, et al. A matching-adjusted indirect comparison of pembrolizumab + chemotherapy vs. nivolumab + ipilimumab as first-line therapies in patients with PD-L1 TPS ≥1% metastatic NSCLC. Cancers (Basel), 2020, 12(12): 3648.
24.	Paz-Ares L, Luft A, Vicente D, et al. Pembrolizumab plus chemotherapy for squamous non-small-cell lung cancer. N Engl J Med, 2018, 379(21): 2040-2051.
25.	Hellmann MD, Paz-Ares L, Bernabe Caro R, et al. Nivolumab plus ipilimumab in advanced non-small-cell lung cancer. N Engl J Med, 2019, 381(21): 2020-2031.
26.	Song F, Harvey I, Lilford R. Adjusted indirect comparison may be less biased than direct comparison for evaluating new pharmaceutical interventions. J Clin Epidemiol, 2008, 61(5): 455-463.

1. 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.
2. 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.
3. Signorovitch JE, Wu EQ, Betts KA, et al. Comparative efficacy of nilotinib and dasatinib in newly diagnosed chronic myeloid leukemia: a matching-adjusted indirect comparison of randomized trials. Curr Med Res Opin, 2011, 27(6): 1263-1271.
4. Garcia-Foncillas J, Bokemeyer C, Italiano A, et al. Indirect treatment comparison of larotrectinib versus entrectinib in treating patients with TRK gene fusion cancers. Cancers (Basel), 2022, 14(7): 1793.
5. Phillippo D, Ades T, Dias 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.
6. 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.
7. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika, 1983, 70(1): 41-55.
8. Austin PC. The performance of different propensity score methods for estimating marginal odds ratios. Stat Med, 2007, 26(16): 3078-3094.
9. 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.
10. 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.
11. Song F, Loke YK, Walsh T, et al. Methodological problems in the use of indirect comparisons for evaluating healthcare interventions: survey of published systematic reviews. BMJ, 2009, 338: b1147.
12. Berlie HD, Kalus JS, Jaber LA. Thiazolidinediones and the risk of edema: a meta-analysis. Diabetes Res Clin Pract, 2007, 76(2): 279-289.
13. Boonen S, Lips P, Bouillon R, et al. Need for additional calcium to reduce the risk of hip fracture with vitamin d supplementation: evidence from a comparative metaanalysis of randomized controlled trials. J Clin Endocrinol Metab, 2007, 92(4): 1415-1423.
14. 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.
15. Lumley T. Network meta-analysis for indirect treatment comparisons. Stat Med, 2002, 21(16): 2313-2324.
16. Flury BK, Riedwyl H. Standard distance in univariate and multivariate analysis. Am Stat, 1986, 40(3): 249-251.
17. Austin PC, Grootendorst P, Anderson GM. A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study. Stat Med, 2007, 26(4): 734-753.
18. Halmos B, Burke T, Kalyvas C, et al. Pembrolizumab + chemotherapy versus atezolizumab + chemotherapy+/-bevacizumab for the first-line treatment of non-squamous NSCLC: a matching-adjusted indirect comparison. Lung Cancer, 2021, 155: 175-182.
19. Awad MM, Gadgeel SM, Borghaei H, et al. Long-term overall survival from KEYNOTE-021 Cohort G: pemetrexed and carboplatin with or without pembrolizumab as first-line therapy for advanced nonsquamous NSCLC. J Thorac Oncol, 2021, 16(1): 162-168.
20. Gandhi L, Rodríguez-Abreu D, Gadgeel S, et al. Pembrolizumab plus chemotherapy in metastatic non-small-cell lung cancer. N Engl J Med, 2018, 378(22): 2078-2092.
21. West H, McCleod M, Hussein M, et al. Atezolizumab in combination with carboplatin plus nab-paclitaxel chemotherapy compared with chemotherapy alone as first-line treatment for metastatic non-squamous non-small-cell lung cancer (IMpower130): a multicentre, randomised, open-label, phase 3 trial. Lancet Oncol, 2019, 20(7): 924-937.
22. Guyot P, Ades AE, Ouwens MJ, et al. Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Med Res Methodol, 2012, 12: 9.
23. Halmos B, Burke T, Kalyvas C, et al. A matching-adjusted indirect comparison of pembrolizumab + chemotherapy vs. nivolumab + ipilimumab as first-line therapies in patients with PD-L1 TPS ≥1% metastatic NSCLC. Cancers (Basel), 2020, 12(12): 3648.
24. Paz-Ares L, Luft A, Vicente D, et al. Pembrolizumab plus chemotherapy for squamous non-small-cell lung cancer. N Engl J Med, 2018, 379(21): 2040-2051.
25. Hellmann MD, Paz-Ares L, Bernabe Caro R, et al. Nivolumab plus ipilimumab in advanced non-small-cell lung cancer. N Engl J Med, 2019, 381(21): 2020-2031.
26. Song F, Harvey I, Lilford R. Adjusted indirect comparison may be less biased than direct comparison for evaluating new pharmaceutical interventions. J Clin Epidemiol, 2008, 61(5): 455-463.

Chinese Journal of Evidence-Based Medicine

Introduction of matching-adjusted indirect comparison in medical research

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