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

重症肌无力患者的网络连接性、研究间异质性和时间点挑战:间接治疗比较的可行性评估

阅读:1

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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。