Calculating statistical power in Mendelian randomization studies
Calculating statistical power in Mendelian randomization studies
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DOI：
10.1093/ije/dyt179
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发表时间：
2013-10-01
影响因子：
7.7
通讯作者：
Visscher, Peter M.
中科院分区：
文献类型：
--
作者：
Brion, Marie-Jo A.;Shakhbazov, Konstantin;Visscher, Peter M.
在孟德尔随机化（MR）研究中，遗传变异被用作感兴趣的暴露性状的替代指标，获得足够的统计功效通常是一个问题，因为通常由遗传变异解释的表型性状的变异量很小。一系列基于模拟和特定参数的功效估计，用于基于连续变量的两阶段最小二乘（2SLS）MR分析。然而，目前没有特定的方程或软件工具可以实现用于计算给定MR研究的功率。使用渐近理论，我们表明，在连续变量和单一工具的情况下，例如单核苷酸多态性（SNP）或多个SNP预测，固定样本量的统计功效是两个参数的函数：遗传预测因子解释的暴露变量的变异比例和暴露与结果变量之间的真正因果关系。我们证明，2SLS MR的功效可以使用统计检验的非中心性参数（NCP）来推导，该统计检验用于检验2SLS回归系数是否为零。我们表明，以前公布的功率估计从模拟可以表示理论上使用这种基于NCP的方法，与类似的估计时，观察到基于模拟的估计与我们的基于NCP的方法相比。本说明中提供了使用NCP计算2SLS MR统计功效的一般方程，我们在基于Web的应用程序中实现了计算。
In Mendelian randomization (MR) studies, where genetic variants are used as proxy measures for an exposure trait of interest, obtaining adequate statistical power is frequently a concern due to the small amount of variation in a phenotypic trait that is typically explained by genetic variants. A range of power estimates based on simulations and specific parameters for two-stage least squares (2SLS) MR analyses based on continuous variables has previously been published. However there are presently no specific equations or software tools one can implement for calculating power of a given MR study. Using asymptotic theory, we show that in the case of continuous variables and a single instrument, for example a single-nucleotide polymorphism (SNP) or multiple SNP predictor, statistical power for a fixed sample size is a function of two parameters: the proportion of variation in the exposure variable explained by the genetic predictor and the true causal association between the exposure and outcome variable. We demonstrate that power for 2SLS MR can be derived using the non-centrality parameter (NCP) of the statistical test that is employed to test whether the 2SLS regression coefficient is zero. We show that the previously published power estimates from simulations can be represented theoretically using this NCP-based approach, with similar estimates observed when the simulation-based estimates are compared with our NCP-based approach. General equations for calculating statistical power for 2SLS MR using the NCP are provided in this note, and we implement the calculations in a web-based application.