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使用单个遗传工具进行孟德尔随机化研究的功效和样本量计算。

Power and sample size calculations for Mendelian randomization studies using one genetic instrument.

机构信息

School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China and CUNY School of Public Health at Hunter College, New York, USA.

出版信息

Int J Epidemiol. 2013 Aug;42(4):1157-63. doi: 10.1093/ije/dyt110. Epub 2013 Aug 9.

Abstract

Mendelian randomization, which is instrumental variable analysis using genetic variants as instruments, is an increasingly popular method of making causal inferences from observational studies. In order to design efficient Mendelian randomization studies, it is essential to calculate the sample sizes required. We present formulas for calculating the power of a Mendelian randomization study using one genetic instrument to detect an effect of a given size, and the minimum sample size required to detect effects for given levels of significance and power, using asymptotic statistical theory. We apply the formulas to some example data and compare the results with those from simulation methods. Power and sample size calculations using these formulas should be more straightforward to carry out than simulation approaches. These formulas make explicit that the sample size needed for Mendelian randomization study is inversely proportional to the square of the correlation between the genetic instrument and the exposure and proportional to the residual variance of the outcome after removing the effect of the exposure, as well as inversely proportional to the square of the effect size.

摘要

孟德尔随机化是一种使用遗传变异作为工具的工具变量分析,是一种从观察性研究中得出因果推论的越来越受欢迎的方法。为了设计有效的孟德尔随机化研究,计算所需的样本量至关重要。我们使用渐近统计理论提出了使用一个遗传工具检测给定大小效应的孟德尔随机化研究的功效和检测给定置信水平和功效的效应所需的最小样本量的公式。我们将这些公式应用于一些示例数据,并将结果与模拟方法进行比较。使用这些公式进行功效和样本量计算应该比模拟方法更简单。这些公式明确指出,孟德尔随机化研究所需的样本量与遗传工具与暴露之间的相关性的平方成反比,与去除暴露效应后结果的剩余方差成正比,并且与效应大小的平方成反比。

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