[Pubmed] Network connectivity, between-study heterogeneity and timepoint challenges in generalized myasthenia gravis: a

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J Comp Eff Res. 2025 May 5:e250009. doi: 10.57264/cer-2025-0009. Online ahead of print.

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.

PMID:40321129 | DOI:10.57264/cer-2025-0009


Source: https://pubmed.ncbi.nlm.nih.gov/4032112 ... t9+e462414