孟德尔随机化中双向因果效应检验的聚焦框架。

IF 3.1 1区 数学 Q1 STATISTICS & PROBABILITY
Sai Li, Ting Ye
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引用次数: 0

摘要

孟德尔随机化(MR)是一种强大的方法,它使用遗传变异作为工具变量来推断可修改暴露对结果的因果关系。我们研究双向因果关系和因果方向的推断与可能的多效性遗传变异。我们表明,考虑到潜在的双向关系,普通MR方法的假设往往是不可能的或过于严格的。我们提出了一个新的聚焦框架来测试双向因果效应,它可以与许多最先进的MR方法相结合。我们为我们的建议提供了理论保证,并使用几个模拟和真实数据集证明了它的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A focusing framework for testing bi-directional causal effects in Mendelian randomization.

Mendelian randomization (MR) is a powerful method that uses genetic variants as instrumental variables to infer the causal effect of a modifiable exposure on an outcome. We study inference for bi-directional causal relationships and causal directions with possibly pleiotropic genetic variants. We show that assumptions for common MR methods are often impossible or too stringent given the potential bi-directional relationships. We propose a new focusing framework for testing bi-directional causal effects and it can be coupled with many state-of-the-art MR methods. We provide theoretical guarantees for our proposal and demonstrate its performance using several simulated and real datasets.

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来源期刊
CiteScore
8.80
自引率
0.00%
发文量
83
审稿时长
>12 weeks
期刊介绍: Series B (Statistical Methodology) aims to publish high quality papers on the methodological aspects of statistics and data science more broadly. The objective of papers should be to contribute to the understanding of statistical methodology and/or to develop and improve statistical methods; any mathematical theory should be directed towards these aims. The kinds of contribution considered include descriptions of new methods of collecting or analysing data, with the underlying theory, an indication of the scope of application and preferably a real example. Also considered are comparisons, critical evaluations and new applications of existing methods, contributions to probability theory which have a clear practical bearing (including the formulation and analysis of stochastic models), statistical computation or simulation where original methodology is involved and original contributions to the foundations of statistical science. Reviews of methodological techniques are also considered. A paper, even if correct and well presented, is likely to be rejected if it only presents straightforward special cases of previously published work, if it is of mathematical interest only, if it is too long in relation to the importance of the new material that it contains or if it is dominated by computations or simulations of a routine nature.
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