Use of the instrumental inequalities in simulated mendelian randomization analyses with coarsened exposures.

IF 7.7 1区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH European Journal of Epidemiology Pub Date : 2024-05-01 Epub Date: 2024-05-31 DOI:10.1007/s10654-024-01130-8
Elizabeth W Diemer, Joy Shi, Miguel A Hernan, Sonja A Swanson
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Abstract

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.

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在粗略暴露的模拟泯灭随机分析中使用工具不等式。
孟德尔随机法(Mendelian randomization,MR)要求对因果效应的估计有很强的不可验证性假设。然而,对于分类暴露,可以使用一种称为工具不等式的方法来验证孟德尔随机化假设。要将工具不等式应用于连续暴露,研究人员必须对暴露进行粗略处理,而这一过程本身就可能违反 MR 条件。因此,对于具有粗化暴露的 MR 模型来说,违反工具不等式可能反映的是粗化的影响,而不是其他偏差来源。我们的目的是评估在不同因果结构下,暴露粗化如何影响工具不等式在具有多个拟议工具的 MR 模型中检测偏差的能力。为此,我们模拟了酒精消费对心血管疾病影响的现有研究数据,这些数据反映了各种暴露-结果效应,其中连续暴露符合 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粗化暴露影响的 MR 模型。在多个二元工具的模拟中,当暴露被粗略分为两个以上类别时,工具不等式在任何暴露结果效应大小下都没有检测到偏差。然而,在模拟单个和多个拟议工具时,当暴露被二分时,在某些情况下违反了工具不等式。这些模拟结果表明,工具不等式对超过 2 个类别的暴露粗化所导致的偏差基本不敏感,因此可用于粗化暴露,以评估应用磁共振研究中所需的假设,即使基础暴露是真正连续的。
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来源期刊
European Journal of Epidemiology
European Journal of Epidemiology 医学-公共卫生、环境卫生与职业卫生
CiteScore
21.40
自引率
1.50%
发文量
109
审稿时长
6-12 weeks
期刊介绍: The European Journal of Epidemiology, established in 1985, is a peer-reviewed publication that provides a platform for discussions on epidemiology in its broadest sense. It covers various aspects of epidemiologic research and statistical methods. The journal facilitates communication between researchers, educators, and practitioners in epidemiology, including those in clinical and community medicine. Contributions from diverse fields such as public health, preventive medicine, clinical medicine, health economics, and computational biology and data science, in relation to health and disease, are encouraged. While accepting submissions from all over the world, the journal particularly emphasizes European topics relevant to epidemiology. The published articles consist of empirical research findings, developments in methodology, and opinion pieces.
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