Mendelian randomization analysis of case-control data using structural mean models
Mendelian randomization analysis of case-control data using structural mean models
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DOI：
10.1002/sim.4138
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发表时间：
2011-03-15
影响因子：
2
通讯作者：
Vansteelandt, Stijn
中科院分区：
文献类型：
--
作者：
Bowden, Jack;Vansteelandt, Stijn
“工具变量”（IV）方法为估计暴露对疾病风险的因果影响提供了基础。在孟德尔随机化研究中，遗传信息在IV中起作用，IV分析通常对病例对照数据进行，而不是前瞻性收集的观察数据。虽然这是一个很好的认识到的事实，确认偏差可能会使这种分析无效，特别的假设和近似值，以证明他们的使用。在本文中，我们试图解释和澄清为什么他们可能会失败，并显示如何调整，以提高性能。特别是，我们提出了一致的因果相对危险度和比值比的估计，如果先验知识是关于人口疾病患病率或人口分布的IV（e）。G.群体等位基因频率）。我们进一步表明，如果没有此类信息可用，则可以在罕见疾病假设下获得近似估计值。我们用最近完成的EPIC研究的病例对照数据来说明这一点，我们试图评估C反应蛋白水平与冠状动脉疾病风险之间因果关系的证据。版权所有（C）2010约翰威利父子有限公司
'Instrumental Variable' (IV) methods provide a basis for estimating an exposure's causal effect on the risk of disease. In Mendelian randomization studies, where genetic information plays the role of the IV, IV analyses are routinely performed on case-control data, rather than prospectively collected observational data. Although it is a well-appreciated fact that ascertainment bias may invalidate such analyses, ad hoc assumptions and approximations are made to justify their use. In this paper we attempt to explain and clarify why they may fail and show how they can be adjusted for improved performance. In particular, we propose consistent estimators of the causal relative risk and odds ratio if a priori knowledge is available regarding either the population disease prevalence or the population distribution of the IV (e. g. population allele frequencies). We further show that if no such information is available, approximate estimators can be obtained under a rare disease assumption. We illustrate this with matched case-control data from the recently completed EPIC study, from which we attempt to assess the evidence for a causal relationship between C-reactive protein levels and the risk of Coronary Artery Disease. Copyright (C) 2010 John Wiley & Sons, Ltd.