Skip to main content
Search
Search
Advanced Search
Skip main navigation
Open access
Research Article
5 May 2025

Network connectivity, between-study heterogeneity and timepoint challenges in generalized myasthenia gravis: a feasibility assessment of indirect treatment comparisons

Authors: Nils Erik Gilhus, Saiju Jacob, Mahmoud Hashim, Suzy Van Sanden, Christopher Drudge https://orcid.org/0000-0001-9721-3069, Anna Nero, Sumeet Singh, Kavita Gandhi [email protected], and Brian Hutton https://orcid.org/0000-0001-5662-8647Author Info & Affiliations
Volume 14, Number 6
https://doi.org/10.57264/cer-2025-0009
PDFFormats

Abstract

Aim: We performed a feasibility assessment to systematically evaluate randomized controlled trials (RCTs) for generalized myasthenia gravis (gMG) treatments. The goal was to identify the advantages and disadvantages of different indirect treatment comparison (ITC) methods. Materials & methods: A systematic literature review was conducted to identify relevant gMG RCTs for ITCs. The feasibility of ITCs was assessed by comparing design (including study duration and dosing schedules), population and outcome characteristics of retrieved trials, investigating network connectivity and considering appropriate ITC methods to address identified challenges. Results: The feasibility assessment considered 15 relevant RCTs for gMG treatments. Several barriers to conducting robust ITCs were identified, including within-trial imbalances in patient characteristics, small trial sizes and cross-trial differences in potential treatment effect modifiers (TEMs; e.g., antibody status, disease duration and prior treatment exposure). Further, heterogeneity in placebo administration characteristics and background therapies, and cross-trial variation in placebo response for key outcomes were noted. Additionally, treatment strategies (i.e., cyclical vs continuous), dosing schedules and outcome assessment timepoints were inconsistent across trials, necessitating careful consideration of methods and timepoints when interpreting outcomes. The findings suggest that ITCs anchored on placebo as a common comparator may be prone to bias, and more than one ITC approach may be necessary. Conclusion: ITC analyses in gMG have inherent challenges related to imbalanced treatment effect modifiers, network connectivity, varying dosing strategies and assessment timepoints. Multiple approaches to ITCs, with careful evaluation of underlying assumptions and limitations, are advised to limit bias and ensure robust comparative efficacy estimates are available to decision makers.

Plain language summary: Investigating differences between clinical trials of treatments for generalized myasthenia gravis to identify appropriate indirect treatment comparison methods

What is this article about?

Generalized myasthenia gravis (gMG) is a rare condition where communication is disrupted between nerves and muscles impacting the ability to see clearly, eat, breathe and do daily activities. There are multiple treatments available for gMG that target this disruption, but their effectiveness has not been directly compared in a clinical trial. In this situation, analyses known as indirect treatment comparisons (ITCs) can inform how gMG treatments compare with each other. There are multiple ITC methods, and they have different requirements, so it is important to closely examine the clinical trials for gMG treatments to see if there are differences between them that could lead to unreliable results. In this work, we examined 15 clinical trials for gMG treatments to identify which ITC methods were most appropriate to generate reliable comparisons.

What were the results?

We identified several barriers to conducting robust ITCs, including differences in the characteristics of patients included in trials, as well as differences in other treatments patients could be receiving during a trial, the way in which treatments were administered, and when patients were evaluated during the trials to determine how effective the treatments were.

What do the results mean?

It is important to use multiple ITC methods to understand how gMG treatment compare with each other, and to carefully evaluate the underlying assumptions and limitations of these methods.

Formats available

You can view the full content in the following formats:

View PDFView Full Text

Supplementary Material

File (supplementary material.docx)

References

Papers of special note have been highlighted as: • of interest
1.
Gelinas D, Parvin-Nejad S, Phillips G et al. The humanistic burden of myasthenia gravis: a systematic literature review. J. Neurol. Sci. 437, 120268 (2022).
PubMed
Google Scholar
2.
Engebretsen I, Gilhus NE, Kristiansen IS et al. The epidemiology and societal costs of myasthenia gravis in Norway: a non-interventional study using national registry data. Eur. J. Neurol. 31(5), e16233 (2024).
PubMed
Google Scholar
3.
Saccà F, Salort-Campana E, Jacob S, Cortés-Vicente E, Schneider-Gold C. Refocusing generalized myasthenia gravis: patient burden, disease profiles, and the role of evolving therapy. Eur. J. Neurol. 31(6), e16180 (2024).
PubMed
Google Scholar
4.
Zhdanava M, Pesa J, Boonmak P et al. Economic burden of generalized myasthenia gravis (MG) in the United States and the impact of common comorbidities and acute MG-related events. Curr. Med. Res. Opin. 40(7), 1145–1153 (2024).
PubMed
Google Scholar
5.
Gilhus NE, Tzartos S, Evoli A, Palace J, Burns TM, Verschuuren JJGM. Myasthenia gravis. Nat. Rev. Dis. Primers. 5(1), 30 (2019).
PubMed
Google Scholar
6.
Sanders DB, Wolfe GI, Benatar M et al. International consensus guidance for management of myasthenia gravis. Neurology 87(4), 419–425 (2016).
PubMed
Google Scholar
7.
Nair SS, Jacob S. Novel immunotherapies for myasthenia gravis. Immunotargets Ther. 12, 25–45 (2023).
PubMed
Google Scholar
8.
Narayanaswami P, Sanders DB, Wolfe G et al. International consensus guidance for management of myasthenia gravis. Neurology 96(3), 114–122 (2021).
PubMed
Google Scholar
9.
Wiendl H, Abicht A, Chan A et al. Guideline for the management of myasthenic syndromes. Ther. Adv. Neurol. Disord. 16, 17562864231213240 (2023).
Google Scholar
10.
Howard JF Jr, Bril V, Vu T et al. Safety, efficacy, and tolerability of efgartigimod in patients with generalised myasthenia gravis (ADAPT): a multicentre, randomised, placebo-controlled, Phase III trial. Lancet Neurol. 20(7), 526–536 (2021).
PubMed
Google Scholar
11.
Bril V, Drużdż A, Grosskreutz J et al. Safety and efficacy of rozanolixizumab in patients with generalised myasthenia gravis (MycarinG): a randomised, double-blind, placebo-controlled, adaptive Phase III study. Lancet Neurol. 22(5), 383–394 (2023).
PubMed
Google Scholar
12.
Antozzi C, Vu T, Ramchandren S et al. Safety and efficacy of nipocalimab in adults with generalised myasthenia gravis (Vivacity-MG3): a Phase III, randomised, double-blind, placebo-controlled study. Lancet Neurol. 24(2), 105–116 (2025).
PubMed
Google Scholar
13.
Howard JF Jr, Utsugisawa K, Benatar M et al. Safety and efficacy of eculizumab in anti-acetylcholine receptor antibody-positive refractory generalised myasthenia gravis (REGAIN): a Phase III, randomised, double-blind, placebo-controlled, multicentre study. Lancet Neurol. 16(12), 976–986 (2017).
PubMed
Google Scholar
14.
Vu T, Meisel A, Mantegazza R et al. Terminal complement inhibitor ravulizumab in generalized myasthenia gravis. NEJM Evid. 1(5), EVIDoa2100066 (2022).
PubMed
Google Scholar
15.
Howard JF Jr, Bresch S, Genge A et al. Safety and efficacy of zilucoplan in patients with generalised myasthenia gravis (RAISE): a randomised, double-blind, placebo-controlled, Phase III study. Lancet Neurol. 22(5), 395–406 (2023).
PubMed
Google Scholar
16.
Salanti G. Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Res. Synth. Methods 3(2), 80–97 (2012).
PubMed
Google Scholar
• Describes network meta-analysis (NMAs), which are one of the indirect treatment comparison (ITC) methods considered in this article.
17.
Signorovitch JE, Sikirica V, Erder MH et al. Matching-adjusted indirect comparisons: a new tool for timely comparative effectiveness research. Value Health 15(6), 940–947 (2012).
PubMed
Google Scholar
• Describes matching-adjusted indirect comparisons (MAICs), which are one of the ITC methods considered in this article.
18.
Lu G, Ades AE. Combination of direct and indirect evidence in mixed treatment comparisons. Stat. Med. 23(20), 3105–3124 (2004).
PubMed
Google Scholar
19.
Bucher HC, Guyatt GH, Griffith LE, Walter SD. The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. J. Clin. Epidemiol. 50(6), 683–691 (1997).
PubMed
Google Scholar
20.
Ishak KJ, Proskorovsky I, Benedict A. Simulation and matching-based approaches for indirect comparison of treatments. Pharmacoeconomics 33(6), 537–549 (2015).
PubMed
Google Scholar
21.
European Union. HTA CG Methodological Guideline for Quantitative Evidence Synthesis: Direct and Indirect Comparisons. (2024). Available from: https://health.ec.europa.eu/publications/methodological-guideline-quantitative-evidence-synthesis-direct-and-indirect-comparisons_en
Google Scholar
• Describes available ITC methods and provides general guidance on which methods are appropriate for particular situations.
22.
Canada's Drug Agency. Reimbursement recommendation for efgartigimod alfa (Vyvgart). (2023). Available from: https://www.cadth.ca/sites/default/files/DRR/2024/SR0782REC-Vyvgart-meta.pdf
Google Scholar
24.
European Medicines Agency. Orphan designation withdrawal assessment report for Zilbrysq (zilucoplan). (2023). Available from: https://www.ema.europa.eu/en/documents/orphan-maintenance-report/zilbrysq-orphan-designation-withdrawal-assessment-report_en.pdf
Google Scholar
25.
Institute for Clinical and Economic Review. Eculizumab and efgartigimod for the treatment of myasthenia gravis: effectiveness and value. Final Evid.Rep. (2021). Available from: https://icer.org/assessment/myasthenia-gravis/
Google Scholar
26.
National Institute for Health and Care Excellence. Ravulizumab for treating generalised myasthenia gravis (terminated appraisal). Technology appraisal TA940. Com.Pap. (2023). Available from: https://www.nice.org.uk/guidance/ta940/documents/committee-papers
Google Scholar
27.
Zhang I, Jansen JP, Yungher BJ, Kielhorn A, Yee KS. Commentary: efficacy and safety of the innovative monoclonal antibodies in adults with generalized myasthenia gravis: a Bayesian network analysis. Front. Immunol. 15, 1403802 (2024).
PubMed
Google Scholar
28.
Ades AE, Caldwell DM, Reken S, Welton NJ, Sutton AJ, Dias S. NICE DSU Technical Support Document 7: evidence synthesis of treatment efficacy in decision making: a reviewer's checklist. (2012). Available from: https://www.sheffield.ac.uk/nice-dsu/tsds/full-list
Google Scholar
29.
Dias S, Sutton AJ, Welton NJ, Ades AE. NICE DSU Technical Support Document 3: heterogeneity: subgroups, meta-regression, bias and bias-adjustment. (2012). Available from: https://www.sheffield.ac.uk/nice-dsu/tsds/full-list
Google Scholar
30.
Higgins Jpt TJ, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (Eds). Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane (2023). Available from: http://www.training.cochrane.org/handbook
Google Scholar
• Provides detailed information on systematic literature reviews and NMAs, including important considerations when conducting these activities.
31.
Hoaglin DC, Hawkins N, Jansen JP et al. Conducting indirect-treatment-comparison and network-meta-analysis studies: Report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: Part 2. Value Health 14(4), 429–437 (2011).
PubMed
Google Scholar
• Provides guidance on conducting ITCs including NMAs.
32.
Cameron C, Hutton B, Druchok C et al. Importance of assessing and adjusting for cross-study heterogeneity in network meta-analysis: a case study of psoriasis. J. Comp. Eff. Res. 7(11), 1037–1051 (2018).
PubMed
Google Scholar
33.
Nikolakopoulou A, Chaimani A, Furukawa TA, Papakonstantinou T, Rücker G, Schwarzer G. When does the placebo effect have an impact on network meta-analysis results? BMJ Evid. Based Med. 29(2), 127 (2024).
PubMed
Google Scholar
34.
Bannuru RR, Mcalindon TE, Sullivan MC, Wong JB, Kent DM, Schmid CH. Effectiveness and implications of alternative placebo treatments. Ann. Intern. Med. 163(5), 365–372 (2015).
PubMed
Google Scholar
35.
Fawsitt CG, Thom H, Regnier Stephane A, Lee Xin Y, Kymes S, Vase L. Comparison of indirect treatment methods in migraine prevention to address differences in mode of administration. J. Comp. Eff. Res. 12(7), e230021 (2023).
PubMed
Google Scholar
36.
Swerts DB, Benedetti F, Peres MFP. Different routes of administration in chronic migraine prevention lead to different placebo responses: a meta-analysis. Pain 163(3), 415–424 (2022).
PubMed
Google Scholar
37.
Cope S, Zhang J, Saletan S, Smiechowski B, Jansen JP, Schmid P. A process for assessing the feasibility of a network meta-analysis: a case study of everolimus in combination with hormonal therapy versus chemotherapy for advanced breast cancer. BMC Med. 12(1), 93 (2014).
PubMed
Google Scholar
• Outlines a process for assessing the feasibility of NMAs.
38.
Page MJ, Mckenzie JE, Bossuyt PM et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Brit. Med. J. 372, n71 (2021).
PubMed
Google Scholar
39.
Wolfe GI, Barohn RJ, Foster BM et al. Randomized, controlled trial of intravenous immunoglobulin in myasthenia gravis. Muscle Nerve 26(4), 549–552 (2002).
PubMed
Google Scholar
40.
European Union. HTA CG Practical Guideline for Quantitative Evidence Synthesis: Direct and Indirect Comparisons. (2024). Available from: https://health.ec.europa.eu/publications/practical-guideline-quantitative-evidence-synthesis-direct-and-indirect-comparisons_en
Google Scholar
• Describes available ITC methods and provides practical guidance on which methods are appropriate for particular situations.
41.
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 14(4), 417–428 (2011).
PubMed
Google Scholar
42.
Phillippo DM, Ades AE, Dias S, Palmer S, Abrams KR, Welton NJ. NICE DSU Technical Support Document 18: methods for population-adjusted indirect comparisons in submission to NICE. (2016). Available from: https://www.sheffield.ac.uk/nice-dsu/tsds/full-list
Google Scholar
43.
Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat. Med. 28(25), 3083–3107 (2009).
PubMed
Google Scholar
44.
ClinicalTrials.gov. Evaluating the pharmacodynamic noninferiority of efgartigimod PH20 SC administered subcutaneously as compared to efgartigimod administered intravenously in patients with generalized myasthenia gravis (ADAPT-SC). (2023). Available from: https://clinicaltrials.gov/study/NCT04735432
Google Scholar
45.
Howard JF, Bril V, Burns TM et al. Randomized Phase II study of FcRn antagonist efgartigimod in generalized myasthenia gravis. Neurology 92(23), e2661–e2673 (2019).
PubMed
Google Scholar
46.
Bril V, Benatar M, Andersen H et al. Efficacy and safety of rozanolixizumab in moderate to severe generalized myasthenia gravis. Neurology 96(6), e853–e865 (2021).
PubMed
Google Scholar
47.
Howard JF Jr, Nowak RJ, Wolfe GI et al. Clinical effects of the self-administered subcutaneous complement inhibitor zilucoplan in patients with moderate to severe generalized myasthenia gravis: results of a Phase II randomized, double-blind, placebo-controlled, multicenter clinical trial. JAMA Neurol. 77(5), 582–592 (2020).
PubMed
Google Scholar
48.
Piehl F, Eriksson-Dufva A, Budzianowska A et al. Efficacy and safety of rituximab for new-onset generalized myasthenia gravis: the RINOMAX randomized clinical trial. JAMA Neurol. 79(11), 1105–1112 (2022).
PubMed
Google Scholar
49.
Nowak RJ, Coffey CS, Goldstein JM et al. Phase II trial of rituximab in acetylcholine receptor antibody-positive generalized myasthenia gravis. Neurology 98(4), e376–e389 (2022).
PubMed
Google Scholar
50.
ClinicalTrials.gov. A study to evaluate the efficacy and safety of IGIV-C in symptomatic subjects with generalized myasthenia gravis. (2019). Available from: https://clinicaltrials.gov/study/NCT02473952
Google Scholar
51.
Bril V, Szczudlik A, Vaitkus A et al. Randomized double-blind placebo-controlled trial of the corticosteroid-sparing effects of immunoglobulin in myasthenia gravis. Neurology 100(7), e671–e682 (2023).
PubMed
Google Scholar
52.
Remiro-Azocar A, Heath A, Baio G. Methods for population adjustment with limited access to individual patient data: a review and simulation study. Res. Synth. Methods 12(6), 750–775 (2021).
PubMed
Google Scholar
53.
Tanaka S, Igarashi A, De Moor R et al. A targeted review of worldwide indirect treatment comparison (ITC) guidelines and best practices. Value Health 27(9), 1179–1190 (2024).
PubMed
Google Scholar
54.
National Institute for Health and Care Excellence. Efgartigimod for treating generalised myasthenia gravis (in development). Technology appraisal ID4003. Com.Pap. (2023). Available from: https://www.nice.org.uk/guidance/gid-ta10986/documents/committee-papers
Google Scholar
55.
Freitag A, Gurskyte L, Sarri G. Increasing transparency in indirect treatment comparisons: is selectingeffect modifiers the missing part of the puzzle? A review of methodological approaches and critical considerations. J. Comp. Eff. Res. 12(10), e230046 (2023).
PubMed
Google Scholar
56.
Habib AA, Zhao C, Aban I et al. Safety and efficacy of satralizumab in patients with generalised myasthenia gravis (LUMINESCE): a randomised, double-blind, multicentre, placebo-controlled Phase III trial. Lancet Neurol. 24(2), 117–127 (2025).
PubMed
Google Scholar
57.
Amgen. Amgen presents positive Phase IIIdata for Uplizna (inebilizumab-cdon) in generalized myasthenia gravis (gMG) at AANEM 2024. (2024). Available from: https://www.amgen.com/newsroom/press-releases/2024/10/amgen-presents-positive-phase-3-data-for-uplizna-inebilizumabcdon-in-generalized-myasthenia-gravis-gmg-at-aanem-2024
Google Scholar
58.
Meltzer-Brody S, Gerbasi ME, Mak C et al. Indirect comparisons of relative efficacy estimates of zuranolone and selective serotonin reuptake inhibitors for postpartum depression. J. Med. Econ. 27(1), 582–595 (2024).
PubMed
Google Scholar
59.
Swallow E, Fang A, Signorovitch J, Plumb J, Borghs S. Can matching-adjusted indirect comparison methods mitigate placebo response differences among patient populations in adjunctive trials of brivaracetam and levetiracetam? CNS Drugs 31(10), 899–910 (2017).
PubMed
Google Scholar
60.
Chen H, Qiu Y, Yin Z et al. Efficacy and safety of the innovative monoclonal antibodies in adults with generalized myasthenia gravis: a Bayesian network analysis. Front. Immunol. 14, 1280226 (2023).
PubMed
Google Scholar
61.
Ma Y, Nie X, Zhu G, Qi W, Hao L, Guo X. The efficacy and safety of different targeted drugs for the treatment of generalized myasthenia gravis: a systematic review and Bayesian network meta-analysis. CNS Drugs 38(2), 93–104 (2024).
PubMed
Google Scholar
62.
Saccà F, Pane C, Espinosa PE, Sormani MP, Signori A. Efficacy of innovative therapies in myasthenia gravis: a systematic review, meta-analysis and network meta-analysis. Eur. J. Neurol. 30(12), 3854–3867 (2023).
PubMed
Google Scholar
63.
Song Z, Zhang J, Meng J et al. Different monoclonal antibodies in myasthenia gravis: a Bayesian network meta-analysis. Front. Pharmacol. 12, 790834 (2022).
PubMed
Google Scholar
64.
Celico L, Spaepen E, De Francesco M, Iannazzo S. An indirect comparison of efgartigimod versus rituximab for generalized myasthenia gravis (abstract CO123). Value Health 25(12), S42 (2022).
Google Scholar
65.
Van Steen C, Celico L, Spaepen E et al. Efgartigimod and ravulizumab for treating acetylcholine receptor auto-antibody-positive (AChR-Ab+) generalized myasthenia gravis: indirect treatment comparison. Adv. Ther. 41, 2486–2499 (2024).
PubMed
Google Scholar
|