The many weak instruments problem and Mendelian randomization.
The many weak instruments problem and Mendelian randomization.
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DOI：
10.1002/sim.6358
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发表时间：
2015-02-10
影响因子：
2
通讯作者：
Smith, George Davey
中科院分区：
文献类型：
--
作者：
Davies, Neil M.;Scholder, Stephanie von Hinke Kessler;Farbmacher, Helmut;Burgess, Stephen;Windmeijer, Frank;Smith, George Davey
关键词：
当使用许多与暴露仅弱相关的工具时，因果效应的工具变量估计值可能有偏差。我们描述了几种技术，以减少这种偏见和估计校正的标准误差。我们提出了我们的研究结果，使用模拟研究和实证应用。对于后者，我们估计身高对肺功能的影响，使用遗传变异作为身高的工具。我们的模拟研究表明，使用许多弱的个体变量，两阶段最小二乘（2SLS）是有偏的，而有限信息最大似然（LIML）和连续更新估计（CUE）是无偏的，并有准确的拒绝频率时，标准误差校正存在许多弱的工具。我们的说明性实证例子使用的数据来自英格兰的3631名儿童。我们使用了180个遗传变异作为工具，并将传统的普通最小二乘估计与使用个体身高变异的2SLS、LIML和CUE工具变量估计的结果进行了比较。我们进一步比较这些与工具变量估计使用未加权或加权等位基因得分作为单一工具。总之，等位基因评分和CUE对因果效应的估计是一致的。在我们的实证例子中，使用等位基因得分的估计更有效。然而，具有校正标准误差的CUE在具有许多弱仪器的应用中提供了有用的附加统计工具。如果等位基因得分的群体权重未知或当联合估计多个风险因素的因果效应时，CUE可能优于等位基因得分。© 2014作者。由John Wiley & Sons Ltd.出版。
Instrumental variable estimates of causal effects can be biased when using many instruments that are only weakly associated with the exposure. We describe several techniques to reduce this bias and estimate corrected standard errors. We present our findings using a simulation study and an empirical application. For the latter, we estimate the effect of height on lung function, using genetic variants as instruments for height. Our simulation study demonstrates that, using many weak individual variants, two-stage least squares (2SLS) is biased, whereas the limited information maximum likelihood (LIML) and the continuously updating estimator (CUE) are unbiased and have accurate rejection frequencies when standard errors are corrected for the presence of many weak instruments. Our illustrative empirical example uses data on 3631 children from England. We used 180 genetic variants as instruments and compared conventional ordinary least squares estimates with results for the 2SLS, LIML, and CUE instrumental variable estimators using the individual height variants. We further compare these with instrumental variable estimates using an unweighted or weighted allele score as single instruments. In conclusion, the allele scores and CUE gave consistent estimates of the causal effect. In our empirical example, estimates using the allele score were more efficient. CUE with corrected standard errors, however, provides a useful additional statistical tool in applications with many weak instruments. The CUE may be preferred over an allele score if the population weights for the allele score are unknown or when the causal effects of multiple risk factors are estimated jointly. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
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影响因子：
6.1
作者：
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