Sibling Comparison Designs: Addressing Confounding Bias with Inclusion of Measured Confounders
Sibling Comparison Designs: Addressing Confounding Bias with Inclusion of Measured Confounders
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DOI：
10.1017/thg.2019.67
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发表时间：
2019-10-01
影响因子：
0.9
通讯作者：
Malone, Stephen M.
中科院分区：
文献类型：
--
作者：
Saunders, Gretchen R. B.;McGue, Matt;Malone, Stephen M.
遗传信息的研究设计越来越受欢迎，作为一种加强因果推理的方式，其控制遗传和共享环境混淆的能力。双胞胎对照（CTC）模型是使用双胞胎样本的设计的一个特例，它将暴露对结果的总体影响分解为双胞胎对内和双胞胎对间的项。理想情况下，双胞胎内项将作为控制遗传和共享环境因素的暴露效应的估计值，但它经常被双胞胎内不共享的因素混淆。先前的模拟工作表明，如果双胞胎在未测量的混杂因素上的相似性低于在暴露上的相似性，则双胞胎对内估计将是对暴露效应的有偏估计，甚至比个体的、未配对的估计更有偏。目前的研究使用模拟和分析推导表明，虽然在CTC模型中纳入与非共享混杂因素相关的协变量总是会降低配对内估计值的偏倚，但仅在狭窄的情况下，它的偏倚低于个体估计值。当暴露量的双胞胎对内相关性小于混杂因素的相关性且协变量的双胞胎对相关性较高时，发生对内估计偏倚降低的最佳情况。此外，还比较了仅调整自己的协变量值和调整自己的值与协变量成对平均值的偏差之间的协变量纳入形式。结果表明，调整双对平均值的偏差会导致相等或减小的偏差。
Genetically informative research designs are becoming increasingly popular as a way to strengthen causal inference with their ability to control for genetic and shared environmental confounding. Co-twin control (CTC) models, a special case of these designs using twin samples, decompose the overall effect of exposure on outcome into a within- and between-twin-pair term. Ideally, the within-twin-pair term would serve as an estimate of the exposure effect controlling for genetic and shared environmental factors, but it is often confounded by factors not shared within a twin-pair. Previous simulation work has shown that if twins are less similar on an unmeasured confounder than they are on an exposure, the within-twin-pair estimate will be a biased estimate of the exposure effect, even more biased than the individual, unpaired estimate. The current study uses simulation and analytical derivations to show that while incorporating a covariate related to the nonshared confounder in CTC models always reduces bias in the within-pair estimate, it will be less biased than the individual estimate only in a narrow set of circumstances. The best case for bias reduction in the within-pair estimate occurs when the within-twin-pair correlation in exposure is less than the correlation in the confounder and the twin-pair correlation in the covariate is high. Additionally, the form of covariate inclusion is compared between adjustment for only one's own covariate value and adjustment for the deviation of one's own value from the covariate twin-pair mean. Results show that adjusting for the deviation from the twin-pair mean results in equal or reduced bias.