Severity of bias of a simple estimator of the causal odds ratio in Mendelian randomization studies
Severity of bias of a simple estimator of the causal odds ratio in Mendelian randomization studies
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DOI：
10.1002/sim.5659
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发表时间：
2013-03-30
影响因子：
2
通讯作者：
Sheehan, Nuala A.
中科院分区：
文献类型：
--
作者：
Harbord, Roger M.;Didelez, Vanessa;Sheehan, Nuala A.
孟德尔随机化研究使用遗传变异作为工具来估计因果效应。工具变量方法对于线性模型来说是直接的，但流行病学家经常使用优势比来量化影响。此外，优势比通常是Meta分析中报告的数量。孟德尔随机化的许多应用将基因型一分为二，并通过将基因-疾病对数优势比除以基因型间平均暴露的差异来估计单位暴露增加的人群因果对数优势比。这种Wald类型的估计量即使在大样本中也是有偏差的，但偏差的大小是否具有实际意义尚不清楚。我们在一个简单模型中研究了这种估计量的大样本偏差，该模型具有连续的正态分布暴露，单个未观察到的混杂因子不是效应修正因子，并且参数是可解释的。我们专注于反映我们应用孟德尔随机化的情景的参数值，包括混杂程度和因果效应强度的实际值。我们用数值积分来估计这个估计量和因果优势比，并得到近似的解析表达式，以检验结果和获得洞察力。一项小型模拟研究考察了有限样本偏差和对正态假设的轻微违反。对于我们的简单数据生成模型，我们发现Wald估计量在相当典型的孟德尔随机化情景中是渐近有偏差的，偏差约为10%，但在更极端的情况下偏差可能更大。最近开发的方法，如结构平均模型，需要较少的不可检验的假设，我们建议在它们需要的个人水平数据可用时使用它们。Wald-type估计器可以保留作为基于汇总数据的Meta分析的近似方法的作用。版权所有(C)2012 John Wiley&Sons，Ltd.
Mendelian randomization studies estimate causal effects using genetic variants as instruments. Instrumental variable methods are straightforward for linear models, but epidemiologists often use odds ratios to quantify effects. Also, odds ratios are often the quantities reported in meta-analyses. Many applications of Mendelian randomization dichotomize genotype and estimate the population causal log odds ratio for unit increase in exposure by dividing the genotype-disease log odds ratio by the difference in mean exposure between genotypes. This Wald-type' estimator is biased even in large samples, but whether the magnitude of bias is of practical importance is unclear. We study the large-sample bias of this estimator in a simple model with a continuous normally distributed exposure, a single unobserved confounder that is not an effect modifier, and interpretable parameters. We focus on parameter values that reflect scenarios in which we apply Mendelian randomization, including realistic values for the degree of confounding and strength of the causal effect. We evaluate this estimator and the causal odds ratio using numerical integration and obtain approximate analytic expressions to check results and gain insight. A small simulation study examines finite sample bias and mild violations of the normality assumption. For our simple data-generating model, we find that the Wald estimator is asymptotically biased with a bias of around 10% in fairly typical Mendelian randomization scenarios but which can be larger in more extreme situations. Recently developed methods such as structural mean models require fewer untestable assumptions and we recommend their use when the individual-level data they require are available. The Wald-type estimator may retain a role as an approximate method for meta-analysis based on summary data. Copyright (c) 2012 John Wiley & Sons, Ltd.