隐于 plain sight：非可缩性如何偏治疗效果于（网络）元分析
Hidden in Plain Sight: How Non-Collapsibility Biases Treatment Effects in (Network) Meta-Analysis
AI总结：
本文探讨了非可缩性如何偏治疗效果，提出书封方法以解决混合人口研究中的偏差问题。
摘要：
网络元分析（NMA）被广泛用于同时比较多种干预措施，通过整合直接和间接证据。通用的固定或随机效应对比基NMA模型可以通过选择臂基或对比基似然来应用于不同的结果和数据结构，这取决于可用的数据。根据结果和链接函数，我们估计可缩或不可缩的效果测量。通过一个涉及二元结果和非可缩比值的示例，我们展示标准NMA模型在证据库中包含异质人口（不同风险组的混合）的研究中，对非可缩效果测量的估计会偏向于空值。重要的是，当研究之间没有效果修饰符差异时，标准假设共同治疗效应在没有研究间效果修饰符分布差异时也不合适，当研究具有不同的基线风险时。作为潜在解决方案，我们提出一种“书封”方法，明确将混合人口研究作为具有极端基线风险的研究中识别出的两个同质亚群体的加权组合，并为从业者提供指导，以确定非可缩性偏是否可能成为问题。
英文摘要：
Network meta-analysis (NMA) is widely used to compare multiple interventions simultaneously by synthesizing direct and indirect evidence. The general fixed or random effects contrast-based NMA model can be applied to different outcomes and data structures by opting for either an arm-based or contrast-based likelihood depending on the data available. Depending on the outcome and link-function, we estimate either collapsible or non-collapsible effect measures. Using an illustrative example involving binary outcomes and the non-collapsible odds ratio, we demonstrate that the standard NMA model produces estimates for non-collapsible effect measures that are biased toward the null when studies in the evidence base enroll heterogeneous populations (mixtures of distinct risk groups) that vary across studies. Importantly, this also holds when there are no differences in effect-modifiers across studies; the standard assumption of a common treatment effect when there are no differences in the distribution of effect-modifiers across studies is not appropriate when studies have different baseline risks. As a potential solution, we propose a ``bookend'' approach that explicitly models mixed-population studies as weighted combinations of two homogeneous subpopulations identified from studies with extreme baseline risks and provide guidance for practitioners to determine if bias due to non-collapsibility may be a concern.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
