Department of Statistics, Florida State University, Tallahassee, FL, USA.
Department of Biostatistics and Research Decision Sciences, Merck & Co., Inc, Rahway, NJ, USA.
BMC Med Res Methodol. 2024 Jul 25;24(1):162. doi: 10.1186/s12874-024-02263-w.
Systematic reviews and meta-analyses are essential tools in contemporary evidence-based medicine, synthesizing evidence from various sources to better inform clinical decision-making. However, the conclusions from different meta-analyses on the same topic can be discrepant, which has raised concerns about their reliability. One reason is that the result of a meta-analysis is sensitive to factors such as study inclusion/exclusion criteria and model assumptions. The arm-based meta-analysis model is growing in importance due to its advantage of including single-arm studies and historical controls with estimation efficiency and its flexibility in drawing conclusions with both marginal and conditional effect measures. Despite its benefits, the inference may heavily depend on the heterogeneity parameters that reflect design and model assumptions. This article aims to evaluate the robustness of meta-analyses using the arm-based model within a Bayesian framework. Specifically, we develop a tipping point analysis of the between-arm correlation parameter to assess the robustness of meta-analysis results. Additionally, we introduce some visualization tools to intuitively display its impact on meta-analysis results. We demonstrate the application of these tools in three real-world meta-analyses, one of which includes single-arm studies.
系统评价和荟萃分析是当代循证医学的重要工具，它综合了来自不同来源的证据，以更好地为临床决策提供信息。然而，对于同一个主题的不同荟萃分析的结论可能存在差异，这引起了人们对其可靠性的关注。其中一个原因是荟萃分析的结果对研究纳入/排除标准和模型假设等因素敏感。基于手臂的荟萃分析模型因其具有纳入单臂研究和历史对照的优势，在估计效率方面具有优势，并且在使用边缘和条件效果测量值得出结论方面具有灵活性，因此越来越受到重视。尽管有这些优势，但推断可能严重依赖于反映设计和模型假设的异质性参数。本文旨在在贝叶斯框架内使用基于手臂的模型评估荟萃分析的稳健性。具体来说，我们开发了一种臂间相关参数的临界点分析，以评估荟萃分析结果的稳健性。此外，我们引入了一些可视化工具，以直观地显示其对荟萃分析结果的影响。我们在三个真实世界的荟萃分析中展示了这些工具的应用，其中一个包括单臂研究。
