Instrumental Variable Estimation of Causal Risk Ratios and Causal Odds Ratios in Mendelian Randomization Analyses
Instrumental Variable Estimation of Causal Risk Ratios and Causal Odds Ratios in Mendelian Randomization Analyses
复制标题
DOI：
10.1093/aje/kwr026
复制
发表时间：
2011-06-15
影响因子：
5
通讯作者：
Didelez, Vanessa
中科院分区：
文献类型：
--
作者：
Palmer, Tom M.;Sterne, Jonathan A. C.;Didelez, Vanessa
在本文中，作者描述了不同的工具变量（IV）因果风险比和比值比的估计，特别注意的方法，可以处理连续测量的风险。作者在孟德尔随机化分析的背景下进行了这一讨论，该分析的目的是研究体重指数（BMI;体重（kg）/身高（m）（2））对7岁时哮喘风险的影响（Avon父母和儿童纵向研究，1991 - 1992）。作者证明了乘性结构均值模型（MSMM）和乘性广义矩法（MGMM）估计量对因果风险比的估计是一致的。在本例中，MSMM和MGMM估计值表明BMI和哮喘之间呈反比关系，但其他IV估计值表明呈正相关关系，尽管所有估计值的置信区间较宽。BMI和脂肪量和肥胖相关（FTO）基因型与哮喘之间的相互作用解释了不同估计的不同方向，模拟研究支持MSMM/MGMM估计值与其他估计值呈负相关的观察，当存在这种相互作用时。作者得出结论，不同IV方法的点估计值在实际应用中可能不同。基于估计量的理论性质，结构均值模型的假设比其他IV估计量弱，因此可以预期在更广泛的情况下是一致的。
In this paper, the authors describe different instrumental variable (IV) estimators of causal risk ratios and odds ratios with particular attention to methods that can handle continuously measured exposures. The authors present this discussion in the context of a Mendelian randomization analysis of the effect of body mass index (BMI; weight (kg)/height (m)(2)) on the risk of asthma at age 7 years (Avon Longitudinal Study of Parents and Children, 19911992). The authors show that the multiplicative structural mean model (MSMM) and the multiplicative generalized method of moments (MGMM) estimator produce identical estimates of the causal risk ratio. In the example, MSMM and MGMM estimates suggested an inverse relation between BMI and asthma but other IV estimates suggested a positive relation, although all estimates had wide confidence intervals. An interaction between the associations of BMI and fat mass and obesity-associated (FTO) genotype with asthma explained the different directions of the different estimates, and a simulation study supported the observation that MSMM/MGMM estimators are negatively correlated with the other estimators when such an interaction is present. The authors conclude that point estimates from various IV methods can differ in practical applications. Based on the theoretical properties of the estimators, structural mean models make weaker assumptions than other IV estimators and can therefore be expected to be consistent in a wider range of situations.