Department of Biostatistics, Columbia University, New York, New York, USA.
Department of Biostatistics, University of Washington, Seattle, Washington, USA.
Biometrics. 2023 Sep;79(3):2208-2219. doi: 10.1111/biom.13735. Epub 2022 Sep 28.
Standard Mendelian randomization (MR) analysis can produce biased results if the genetic variant defining an instrumental variable (IV) is confounded and/or has a horizontal pleiotropic effect on the outcome of interest not mediated by the treatment variable. We provide novel identification conditions for the causal effect of a treatment in the presence of unmeasured confounding by leveraging a possibly invalid IV for which both the IV independence and exclusion restriction assumptions may be violated. The proposed Mendelian randomization mixed-scale treatment effect robust identification (MR MiSTERI) approach relies on (i) an assumption that the treatment effect does not vary with the possibly invalid IV on the additive scale; (ii) that the confounding bias does not vary with the possibly invalid IV on the odds ratio scale; and (iii) that the residual variance for the outcome is heteroskedastic with respect to the possibly invalid IV. Although assumptions (i) and (ii) have, respectively, appeared in the IV literature, assumption (iii) has not; we formally establish that their conjunction can identify a causal effect even with an invalid IV. MR MiSTERI is shown to be particularly advantageous in the presence of pervasive heterogeneity of pleiotropic effects on the additive scale. We propose a simple and consistent three-stage estimator that can be used as a preliminary estimator to a carefully constructed efficient one-step-update estimator. In order to incorporate multiple, possibly correlated, and weak invalid IVs, a common challenge in MR studies, we develop a MAny Weak Invalid Instruments (MR MaWII MiSTERI) approach for strengthened identification and improved estimation accuracy. Both simulation studies and UK Biobank data analysis results demonstrate the robustness of the proposed methods.
标准孟德尔随机化 (MR) 分析如果定义工具变量 (IV) 的遗传变异存在混杂且/或对感兴趣的结局有水平的平行多效性作用，而这种作用不受治疗变量介导，那么分析结果可能会产生偏差。我们利用一个可能无效的 IV（其 IV 独立性和排除限制假设都可能被违反），提供了在存在未测量混杂时处理效果因果关系的新识别条件。拟议的孟德尔随机化混合尺度处理效应稳健识别 (MR MiSTERI) 方法依赖于：(i) 处理效应在加性尺度上不会随可能无效的 IV 变化的假设；(ii) 混杂偏差在比值比尺度上不会随可能无效的 IV 变化的假设；(iii) 结局的残差方差在可能无效的 IV 上是异方差的。尽管假设 (i) 和 (ii) 分别出现在 IV 文献中，但假设 (iii) 尚未出现；我们正式证明，即使使用无效 IV，它们的结合也可以识别因果效应。MR MiSTERI 在加性尺度上存在普遍的平行多效性效应异质性的情况下特别有利。我们提出了一种简单而一致的三阶段估计量，可以作为精心构建的有效一步更新估计量的初步估计量。为了结合多个可能相关且较弱的无效 IV，这是 MR 研究中的一个常见挑战，我们开发了一种用于加强识别和提高估计准确性的许多弱无效工具 (MR MaWII MiSTERI) 方法。模拟研究和英国生物库数据分析结果均证明了所提出方法的稳健性。
