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Abstract：
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.
摘要：
孟德尔随机化（MR）是流行病学调查中的一种既定技术，使用随机分配原则的遗传变异在概念估计的因果线性效应的暴露结果。对该技术的扩展包括非线性方法,其允许取决于曝光水平的曝光对结果的不同影响。一种广泛使用的非线性方法是残差法，它估计了非遗传预测暴露(即“残差暴露”)不同层次内的因果效应。然后使用这些“局部”因果估计来推断非线性效应。最近的工作已经确定，这种方法可能导致严重偏差的估计，并引入了一种新方法-双重排序方法-作为一种可能更稳健的方法。在本文中,我们在MR背景下进行阴性对照结果分析.这些是具有结果的分析，暴露不应具有预测的因果关系。使用这两种方法，我们发现在某些情况下有明显偏差的估计。我们还研究了一种情况，在这种情况下，有可靠的随机对照试验估计的效果-低密度脂蛋白胆固醇（LDL-C）降低对心肌梗死的影响。随机试验为这种关系的形式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，对于LDL-C和其他脂质，他们产生了一些非常令人难以置信的发现。因此,我们建议在进一步使用这些方法之前，应该对两种NLMR方法在不同条件下的性能进行广泛的模拟和经验方法学检查。在此期间，NLMR方法的使用需要证明，和一些理智检查(如分析阴性和阳性对照结果，敏感性分析，不包括在分布的极端位置去除地层，应检查生物合理性和结果的三角测量)。
