On the choice of parameterisation and priors for the Bayesian analyses of Mendelian randomisation studies
On the choice of parameterisation and priors for the Bayesian analyses of Mendelian randomisation studies
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DOI：
10.1002/sim.4499
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发表时间：
2012-06-30
影响因子：
2
通讯作者：
Sheehan, N. A.
中科院分区：
文献类型：
--
作者：
Jones, E. M.;Thompson, J. R.;Sheehan, N. A.
孟德尔随机化是工具变量分析的一种形式，它使用遗传变异作为工具，在存在未观察到的混杂因素的情况下估计中间表型或暴露对结果或疾病的因果影响。贝叶斯方法允许以信息丰富的先验分布的形式将当前知识纳入分析中，并且可以对未观察到的混杂因素进行显式建模。在所有关系都是线性且没有相互作用的情况下，我们考虑孟德尔随机化的贝叶斯方法。尽管可以估计因果参数，但明确包含未观察到的混杂因素的完整模型并不完全可识别。我们将这个一般但未识别的模型的推论与可识别的简化参数模型进行比较。我们表明，从理论上讲，可以通过使用不可识别的完整模型而不是可识别的简化模型来获得有关因果参数的附加信息，但这只有在使用实际信息丰富的先验以及工具较弱或样本量较小时才有优势。此外，我们还考虑了使用模糊先验与信息丰富先验的影响。版权所有 (C) 2012 约翰·威利父子有限公司
Mendelian randomisation is a form of instrumental variable analysis that estimates the causal effect of an intermediate phenotype or exposure on an outcome or disease in the presence of unobserved confounding, using a genetic variant as the instrument. A Bayesian approach allows current knowledge to be incorporated into the analysis in the form of informative prior distributions, and the unobserved confounder can be modelled explicitly. We consider Bayesian methods for Mendelian randomisation in the case where all relationships are linear and there are no interactions. A full model in which the unobserved confounder is included explicitly is not completely identifiable, although the causal parameter can be estimated. We compare inferences from this general but non-identified model with a reduced parameter model that is identifiable. We show that, theoretically, additional information about the causal parameter can be obtained by using the non-identifiable full model, rather than the identifiable reduced model, but that this is advantageous only when realistically informative priors are used and when the instrument is weak or the sample size is small. Furthermore, we consider the impact of using vague versus informative priors. Copyright (C) 2012 John Wiley & Sons, Ltd.