AI 中文总结
该研究探讨网络荟萃分析，提出应先确定因果估计量再汇总，开发统一因果框架，基于汇总数据定义治疗效果、考虑异质性，使组间汇总自然产生，与传统范式不同，数值研究显示新方法因果效应明确，两种方法有差异。
AI 中文摘要
成对和网络荟萃分析在循证医学中处于最高级别，常用于指导临床指南和医疗决策。当前方法通常汇总研究层面的治疗效果以获得总体估计。我们认为应先确定因果估计量，再进行汇总：在推导识别所需汇总之前，应明确目标人群和研究间异质性的相关来源。这种视角转变从根本上改变了估计量和方法。我们基于汇总数据为成对和网络荟萃分析开发了统一的因果框架。通过针对有临床意义的目标人群定义治疗效果，并考虑治疗效果修饰因子和中心效应引起的异质性，我们表明识别自然会导致组间汇总。在网络环境中，这种因果表述与传统的基于对比的范式有根本不同：组间汇总源于因果表述而非建模选择，且治疗效果的识别不依赖于治疗网络本身。数值研究表明，所提出的估计量针对明确的因果效应，而传统方法的因果解释仍不明确。虽然两种方法通常产生相似估计，但我们确定了它们存在差异的情况，这对荟萃分析证据的解释可能有重要影响。
英文摘要
Pairwise and network meta-analyses occupy the highest tier of evidence-based medicine and routinely inform clinical guidelines and healthcare decision-making. Current approaches typically aggregate study-level treatment effects to obtain an overall estimate. We argue that the causal estimand should come first, with the aggregation derived only afterwards: the target population and the relevant sources of between-study heterogeneity should be explicitly defined before deriving the aggregation required for identification. This shift in perspective fundamentally changes both the estimands and the methodology. We develop a unified causal framework for pairwise and network meta-analysis based on aggregate data. By defining treatment effects with respect to a clinically meaningful target population, for example, the average population represented by the contributing trials, and accounting for heterogeneity induced by treatment-effect modifiers and center effects, we show that identification naturally leads to arm-level aggregation. In the network setting, this causal formulation departs fundamentally from the conventional contrast-based paradigm: arm-level aggregation emerges from the causal formulation rather than from a modeling choice, and treatment effects are identified without relying on the treatment network itself. This perspective provides an additional conceptual argument in the long-standing contrast-based versus arm-based debate. Numerical studies show that the proposed estimators target well-defined causal effects, whereas the causal interpretation of conventional approaches remains unclear. Although both approaches often produce similar estimates, we identify settings in which they diverge, with potentially important implications for the interpretation of meta-analytic evidence.
