Estimating Structural Mean Models with Multiple Instrumental Variables Using the Generalised Method of Moments
Estimating Structural Mean Models with Multiple Instrumental Variables Using the Generalised Method of Moments
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DOI：
10.1214/14-sts503
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发表时间：
2015-02-01
影响因子：
5.7
通讯作者：
Windmeijer, Frank
中科院分区：
文献类型：
--
作者：
Clarke, Paul S.;Palmer, Tom M.;Windmeijer, Frank
以遗传标记为工具的工具变量分析是流行病学和生物统计学中广泛应用的技术。由于单个标记往往只能解释一小部分表型变异，因此越来越多的人对使用多个遗传标记来获得更精确的因果参数估计值感兴趣。结构均值模型（SMM）是使用工具变量来识别因果参数的半参数模型。最近，人们的兴趣开始集中在使用这些模型与多个工具，特别是乘法和逻辑SMM。在本文中，我们将展示如何添加剂，乘法和逻辑SMM与多个正交二进制工具变量可以有效地估计模型没有进一步的（连续）协变量，使用广义矩（GMM）估计。我们讨论了如何可以使用汉森J-检验来测试模型误设，以及如何可以使用标准GMM软件例程来拟合SMM。我们进一步表明，乘法SMM，如添加剂SMM，确定加权平均的局部因果效应，如果选择是单调的。我们使用这些方法来重新分析肥胖和高血压之间的关系的研究，使用SMM与两个遗传标记作为肥胖的工具。我们发现肥胖对高血压有很强的影响。
Instrumental variables analysis using genetic markers as instruments is now a widely used technique in epidemiology and biostatistics. As single markers tend to explain only a small proportion of phenotypic variation, there is increasing interest in using multiple genetic markers to obtain more precise estimates of causal parameters. Structural mean models (SMMs) are semiparametric models that use instrumental variables to identify causal parameters. Recently, interest has started to focus on using these models with multiple instruments, particularly for multiplicative and logistic SMMs. In this paper we show how additive, multiplicative and logistic SMMs with multiple orthogonal binary instrumental variables can be estimated efficiently in models with no further (continuous) covariates, using the generalised method of moments (GMM) estimator. We discuss how the Hansen J-test can be used to test for model misspecification, and how standard GMM software routines can be used to fit SMMs. We further show that multiplicative SMMs, like the additive SMM, identify a weighted average of local causal effects if selection is monotonic. We use these methods to reanalyse a study of the relationship between adiposity and hypertension using SMMs with two genetic markers as instruments for adiposity. We find strong effects of adiposity on hypertension.