Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115, USA.
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA.
Eur J Epidemiol. 2024 May;39(5):491-499. doi: 10.1007/s10654-024-01130-8. Epub 2024 May 31.
Mendelian randomization (MR) requires strong unverifiable assumptions to estimate causal effects. However, for categorical exposures, the MR assumptions can be falsified using a method known as the instrumental inequalities. To apply the instrumental inequalities to a continuous exposure, investigators must coarsen the exposure, a process which can itself violate the MR conditions. Violations of the instrumental inequalities for an MR model with a coarsened exposure might therefore reflect the effect of coarsening rather than other sources of bias. We aim to evaluate how exposure coarsening affects the ability of the instrumental inequalities to detect bias in MR models with multiple proposed instruments under various causal structures. To do so, we simulated data mirroring existing studies of the effect of alcohol consumption on cardiovascular disease under a variety of exposure-outcome effects in which the MR assumptions were met for a continuous exposure. We categorized the exposure based on subject matter knowledge or the observed data distribution and applied the instrumental inequalities to MR models for the effects of the coarsened exposure. In simulations of multiple binary instruments, the instrumental inequalities did not detect bias under any magnitude of exposure outcome effect when the exposure was coarsened into more than 2 categories. However, in simulations of both single and multiple proposed instruments, the instrumental inequalities were violated in some scenarios when the exposure was dichotomized. The results of these simulations suggest that the instrumental inequalities are largely insensitive to bias due to exposure coarsening with greater than 2 categories, and could be used with coarsened exposures to evaluate the required assumptions in applied MR studies, even when the underlying exposure is truly continuous.
孟德尔随机化（MR）需要强有力的不可验证的假设来估计因果效应。然而，对于分类暴露，MR 假设可以使用一种称为工具性不平等的方法来验证。为了将工具性不平等应用于连续暴露，研究人员必须对暴露进行粗化，这一过程本身可能违反 MR 条件。因此，MR 模型中暴露粗化的工具性不平等的违反可能反映了粗化的影响，而不是其他偏倚来源。我们旨在评估在各种因果结构下，当使用多个建议工具时，暴露粗化如何影响工具性不平等检测 MR 模型中偏倚的能力。为此，我们模拟了现有的酒精消费对心血管疾病影响的研究数据，在这些研究中，连续暴露的 MR 假设得到了满足。我们根据主题知识或观察到的数据分布对暴露进行分类，并将工具性不平等应用于粗化暴露的 MR 模型。在多个二元工具的模拟中，当暴露被分为超过 2 类时，工具性不平等在任何暴露结果效应的大小下都无法检测到偏倚。然而，在单和多个建议工具的模拟中，当暴露被二分类时，在某些情况下，工具性不平等被违反。这些模拟的结果表明，工具性不平等对大于 2 类的暴露粗化引起的偏倚基本上不敏感，并且可以与粗化暴露一起用于评估应用 MR 研究中的所需假设，即使基础暴露确实是连续的。
