Mendelian randomisation (MR) is an established technique in epidemiological investigation, using the principle of random allocation of genetic variants at conception to estimate the causal linear effect of an exposure on an outcome. Extensions to this technique include non-linear approaches that allow for differential effects of the exposure on the outcome depending on the level of the exposure. A widely used non-linear method is the residual approach, which estimates the causal effect within different strata of the non-genetically predicted exposure (i.e. the “residual” exposure). These “local” causal estimates are then used to make inferences about non-linear effects. Recent work has identified that this method can lead to estimates that are seriously biased, and a new method—the doubly-ranked method—has been introduced as a possibly more robust approach. In this paper, we perform negative control outcome analyses in the MR context. These are analyses with outcomes onto which the exposure should have no predicted causal effect. Using both methods we find clearly biased estimates in certain situations. We additionally examined a situation for which there are robust randomised controlled trial estimates of effects—that of low-density lipoprotein cholesterol (LDL-C) reduction onto myocardial infarction, where randomised trials have provided strong evidence of the shape of the relationship. The doubly-ranked method did not identify the same shape as the trial data, and for LDL-C and other lipids they generated some highly implausible findings. Therefore, we suggest there should be extensive simulation and empirical methodological examination of performance of both methods for NLMR under different conditions before further use of these methods. In the interim, use of NLMR methods needs justification, and a number of sanity checks (such as analysis of negative and positive control outcomes, sensitivity analyses excluding removal of strata at the extremes of the distribution, examination of biological plausibility and triangulation of results) should be performed.

孟德尔随机化 (MR) 是流行病学调查中的一项既定技术，利用受孕时遗传变异的随机分配原理来估计暴露对结果的因果线性影响。该技术的扩展包括非线性方法，允许根据暴露水平对结果产生不同的影响。一种广泛使用的非线性方法是残差法，它估计非基因预测暴露的不同层次内的因果效应（即“残差”暴露）。然后使用这些“局部”因果估计来推断非线性效应。最近的研究发现，这种方法可能会导致估计存在严重偏差，并且引入了一种新方法（双排序方法）作为可能更稳健的方法。在本文中，我们在 MR 背景下进行阴性对照结果分析。这些分析结果的暴露不应产生可预测的因果效应。使用这两种方法，我们发现在某些情况下估计存在明显偏差。我们还研究了一种情况，即对降低低密度脂蛋白胆固醇（LDL-C）对心肌梗死的影响进行了强有力的随机对照试验评估，随机试验为这种关系的形状提供了强有力的证据。双重排序方法没有识别出与试验数据相同的形状，并且对于 LDL-C 和其他脂质，他们产生了一些非常难以置信的结果。因此，我们建议在进一步使用这些方法之前，应对不同条件下这两种方法的性能进行广泛的模拟和实证方法学检查。 在此期间，NLMR 方法的使用需要论证，并进行一些健全性检查（例如阴性和阳性对照结果的分析、敏感性分析（排除分布极端处的层的去除、生物学合理性的检查和结果的三角测量））应该执行。