全阳性编码下的INSIDE假设：解释与部分实证评估

中文摘要 AI 辅助

孟德尔随机化（MR）通过工具变量（IV）分析实施，是观察性研究中增强因果推断的常用策略。包括MR-Egger回归在内的若干MR估计量的一个关键假设是工具强度独立于直接效应（INSIDE）假设。然而，目前尚无既定的实证检验来评估该假设的合理性。此外，INSIDE取决于遗传变异的编码方式（即效应等位基因的选择），这种选择通常具有任意性，因此妨碍了基于实质性理由评估该假设的合理性。在本文中，我们证明全阳性编码方案（即对所有变异，选择与暴露正相关的等位基因作为效应等位基因），该方案通常在MR-Egger中使用，等价于一个编码不变的模型，该模型可以给出自然的解释，因为在此编码方案下，直接效应参数与单个变异比值估计量的偏倚方向相同。此外，利用理论论证和模拟，我们表明，在MR方法学文献中通常假设的数据生成模型下，工具-结局系数相对于工具-暴露系数的异方差性是至少某些类型的INSIDE违反的特征，这表明异方差性检验可能有助于评估INSIDE假设的合理性。我们进一步强调了检验不起作用的特定情况。我们通过重新分析一个评估大颗粒高密度脂蛋白胆固醇对年龄相关性黄斑变性因果效应的真实数据集来说明其应用。

英文摘要

Mendelian randomisation (MR) implemented through instrumental variable (IV) analysis is a popular strategy for strengthening causal inference in observational studies. A key assumption for several MR estimators, including MR-Egger regression, is the INstrument Strength Independent of Direct Effect (INSIDE) assumption. However, there is no established empirical test for assessing the plausibility of this assumption. Moreover, INSIDE depends on how genetic variants are coded (i.e., on the choice of the effect allele), which is often arbitrary and therefore hampers assessing the plausibility of this assumption on substantive grounds. In this paper, we show that the all-positive coding scheme (i.e., for all variants, choosing the allele positively associated with the exposure as the effect allele), which is typically used in MR-Egger, is equivalent to a coding-invariant model that can be given a natural interpretation because the direct effect parameters under this coding scheme are in the same direction as the bias of individual-variant ratio estimators. Moreover, using both theoretical arguments and simulations, we show that, under commonly assumed data-generating models in the MR methodological literature, heteroscedasticity of instrument-outcome coefficients according to instrument-exposure coefficients is a feature of at least some types of INSIDE violation, indicating that heteroscedasticity tests could contribute to assessing the plausibility of the INSIDE assumption. We further highlight specific cases where the test would not work. We illustrate its application by re-analysing a real dataset assessing the causal effect of large particle high density lipoprotein cholesterol on age-related macular degeneration.

发表机构

• Federal University of Pelotas（佩洛塔斯联邦大学）
• MRC Integrative Epidemiology Unit, University of Bristol（布里斯托尔大学 MRC 整合流行病学单元）
• Population Health Sciences, University of Bristol（布里斯托尔大学 人口健康科学系）
• Division of Public Health and Epidemiology, School of Medical Sciences, University of Leicester（莱斯特大学医学院公共卫生与流行病学系）
• Department of Clinical Biosciences, Exeter Medical School, University of Exeter（埃克塞特大学埃克塞特医学院临床生物科学系）

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