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、无法验证的假设来估计因果效应。然而，对于分类暴露，MR 假设可以使用称为工具不等式的方法来证伪。为了将工具不等式应用于连续曝光，研究人员必须粗化曝光，这一过程本身可能违反 MR 条件。因此，对于具有粗化暴露的 MR 模型，违反工具不等式可能反映了粗化的影响，而不是其他偏差来源。我们的目的是评估暴露粗化如何影响工具不等式在各种因果结构下使用多种拟议工具检测 MR 模型中偏差的能力。为此，我们模拟了数据，反映了在各种暴露结果影响下饮酒对心血管疾病影响的现有研究，其中连续暴露满足 MR 假设。我们根据主题知识或观察到的数据分布对暴露进行分类，并将工具不等式应用于 MR 模型，以了解粗化暴露的影响。在多个二元工具的模拟中，当暴露粗化为超过 2 个类别时，工具不等式在任何程度的暴露结果效应下都没有检测到偏差。然而，在对单个和多个拟议工具的模拟中，当暴露被二分时，在某些情况下违反了工具不等式。 这些模拟的结果表明，由于超过 2 个类别的暴露粗化，工具不等式在很大程度上对偏差不敏感，并且可以与粗化暴露一起使用来评估应用 MR 研究中所需的假设，即使基础暴露确实是连续的。