Abstract Random-effects meta-analyses of observational studies can produce biased estimates if the synthesized studies are subject to unmeasured confounding. We propose sensitivity analyses quantifying the extent to which unmeasured confounding of specified magnitude could reduce to below a certain threshold the proportion of true effect sizes that are scientifically meaningful. We also develop converse methods to estimate the strength of confounding capable of reducing the proportion of scientifically meaningful true effects to below a chosen threshold. These methods apply when a “bias factor” is assumed to be normally distributed across studies or is assessed across a range of fixed values. Our estimators are derived using recently proposed sharp bounds on confounding bias within a single study that do not make assumptions regarding the unmeasured confounders themselves or the functional form of their relationships with the exposure and outcome of interest. We provide an R package, EValue, and a free website that compute point estimates and inference and produce plots for conducting such sensitivity analyses. These methods facilitate principled use of random-effects meta-analyses of observational studies to assess the strength of causal evidence for a hypothesis. Supplementary materials for this article are available online.

中文翻译：

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荟萃分析中未测量混杂因素的敏感性分析


摘要：如果综合研究受到无法测量的混杂因素的影响，观察性研究的随机效应荟萃分析可能会产生有偏差的估计。我们建议进行敏感性分析，量化指定幅度的未测量混杂因素可能将具有科学意义的真实效应大小的比例降低到某个阈值以下的程度。我们还开发了相反的方法来估计混杂的强度，能够将具有科学意义的真实效应的比例降低到选定的阈值以下。当假设“偏差因子”在研究中呈正态分布或在一系列固定值中进行评估时，这些方法适用。我们的估计量是使用最近提出的一项研究中混杂偏差的尖锐界限得出的，该研究不对未测量的混杂因素本身或其与感兴趣的暴露和结果的关系的功能形式做出假设。我们提供 R 包、EValue 和免费网站，用于计算点估计和推理，并生成用于进行此类敏感性分析的图。这些方法有助于原则性地使用观察性研究的随机效应荟萃分析来评估假设的因果证据的强度。本文的补充材料可在线获取。