估计因果效应中矩阵暴露的加权欧几里得平衡。

IF 1.2 4区 数学
Juan Chen, Yingchun Zhou
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引用次数: 0

摘要

随着数据的日益复杂,各个领域的研究人员对估计矩阵暴露的因果效应越来越感兴趣,这涉及到复杂的多变量处理。平衡矩阵暴露的协变量对于实现这一目标至关重要。虽然已经提出了针对多个平衡约束的精确平衡和近似平衡方法,但处理矩阵处理引入了大量约束,使得实现精确平衡或为近似平衡方法选择合适的阈值参数具有挑战性。为了解决这一挑战,提出了加权欧几里得平衡方法,该方法从整体角度提供了协变量的近似平衡。在本研究中,提出了估计矩阵处理因果效应的参数和非参数方法,并提供了这两种估计的理论性质。为了验证我们方法的有效性,大量的仿真结果表明,所提出的方法在各种情况下优于其他方法。最后,我们应用该方法分析了组学变量对Vandetanib药物敏感性的因果影响。结果表明,EGFR CNV对Vandetanib疗效有显著的正向因果效应,而EGFR甲基化对Vandetanib疗效有显著的负向因果效应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Weighted Euclidean balancing for a matrix exposure in estimating causal effect.

With the increasing complexity of data, researchers in various fields have become increasingly interested in estimating the causal effect of a matrix exposure, which involves complex multivariate treatments, on an outcome. Balancing covariates for the matrix exposure is essential to achieve this goal. While exact balancing and approximate balancing methods have been proposed for multiple balancing constraints, dealing with a matrix treatment introduces a large number of constraints, making it challenging to achieve exact balance or select suitable threshold parameters for approximate balancing methods. To address this challenge, the weighted Euclidean balancing method is proposed, which offers an approximate balance of covariates from an overall perspective. In this study, both parametric and nonparametric methods for estimating the causal effect of a matrix treatment is proposed, along with providing theoretical properties of the two estimations. To validate the effectiveness of our approach, extensive simulation results demonstrate that the proposed method outperforms alternative approaches across various scenarios. Finally, we apply the method to analyze the causal impact of the omics variables on the drug sensitivity of Vandetanib. The results indicate that EGFR CNV has a significant positive causal effect on Vandetanib efficacy, whereas EGFR methylation exerts a significant negative causal effect.

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来源期刊
International Journal of Biostatistics
International Journal of Biostatistics Mathematics-Statistics and Probability
CiteScore
2.30
自引率
8.30%
发文量
28
期刊介绍: The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.
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