MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom.
British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.
PLoS Genet. 2023 Jun 30;19(6):e1010823. doi: 10.1371/journal.pgen.1010823. eCollection 2023 Jun.
Non-linear Mendelian randomization is an extension to standard Mendelian randomization to explore the shape of the causal relationship between an exposure and outcome using an instrumental variable. A stratification approach to non-linear Mendelian randomization divides the population into strata and calculates separate instrumental variable estimates in each stratum. However, the standard implementation of stratification, referred to as the residual method, relies on strong parametric assumptions of linearity and homogeneity between the instrument and the exposure to form the strata. If these stratification assumptions are violated, the instrumental variable assumptions may be violated in the strata even if they are satisfied in the population, resulting in misleading estimates. We propose a new stratification method, referred to as the doubly-ranked method, that does not require strict parametric assumptions to create strata with different average levels of the exposure such that the instrumental variable assumptions are satisfied within the strata. Our simulation study indicates that the doubly-ranked method can obtain unbiased stratum-specific estimates and appropriate coverage rates even when the effect of the instrument on the exposure is non-linear or heterogeneous. Moreover, it can also provide unbiased estimates when the exposure is coarsened (that is, rounded, binned into categories, or truncated), a scenario that is common in applied practice and leads to substantial bias in the residual method. We applied the proposed doubly-ranked method to investigate the effect of alcohol intake on systolic blood pressure, and found evidence of a positive effect of alcohol intake, particularly at higher levels of alcohol consumption.
非线性孟德尔随机化是对标准孟德尔随机化的扩展，用于使用工具变量探索暴露与结局之间因果关系的形状。非线性孟德尔随机化的分层方法将人群分为 strata，并在每个 strata 中计算单独的工具变量估计值。然而，分层的标准实现，称为残差法，依赖于工具变量与暴露之间线性和同质性的严格参数假设来形成 strata。如果违反了这些分层假设，即使在人群中满足工具变量假设，在 strata 中也可能违反工具变量假设，导致误导性估计。我们提出了一种新的分层方法，称为双重排序法，它不需要严格的参数假设来创建具有不同暴露平均水平的 strata，从而使工具变量假设在 strata 内得到满足。我们的模拟研究表明，即使工具变量对暴露的影响是非线性或异质的，双重排序法也可以获得无偏的 strata 特异性估计值和适当的覆盖率。此外，当暴露被细化（即四舍五入、分类成类别或截断）时，它也可以提供无偏估计值，这种情况在实际应用中很常见，会导致残差法产生很大的偏差。我们应用所提出的双重排序法来研究饮酒量对收缩压的影响，结果发现饮酒量对收缩压有正向影响的证据，尤其是在较高的饮酒量水平下。
