Cross-direct effects in settings with two mediators.

IF 1.8 3区 数学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Erin E Gabriel, Arvid Sjölander, Dean Follmann, Michael C Sachs
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引用次数: 0

Abstract

When multiple mediators are present, there are additional effects that may be of interest beyond the well-known natural (NDE) and controlled direct effects (CDE). These effects cross the type of control on the mediators, setting one to a constant level and one to its natural level, which differs across subjects. We introduce five such estimands for the cross-CDE and -NDE when two mediators are measured. We consider both the scenario where one mediator is influenced by the other, referred to as sequential mediators, and the scenario where the mediators do not influence each other. Such estimands may be of interest in immunology, as we discuss in relation to measured immunological responses to SARS-CoV-2 vaccination. We provide identifying expressions for the estimands in observational settings where there is no residual confounding, and where intervention, outcome, and mediators are of arbitrary type. We further provide tight symbolic bounds for the estimands in randomized settings where there may be residual confounding of the outcome and mediator relationship and all measured variables are binary.

Abstract Image

Abstract Image

Abstract Image

具有两个介质的环境中的交叉直接效应。
当存在多种介质时,除了众所周知的自然效应(NDE)和受控直接效应(CDE)外,还有其他可能引起兴趣的效应。这些影响跨越了对介质的控制类型,将一个设置为恒定水平,另一个设置到其自然水平,这在不同的受试者中有所不同。我们介绍了当测量两种介质时,对交叉CDE和-NDE的五种这样的估计。我们既考虑一个中介受另一个影响的情况,称为顺序中介,也考虑中介不相互影响的情况。正如我们在对严重急性呼吸系统综合征冠状病毒2型疫苗免疫反应的测量中所讨论的那样,这些估计可能对免疫学感兴趣。我们提供了在没有残余混杂的观察环境中,以及干预、结果和中介是任意类型的情况下,估计的识别表达。我们进一步为随机环境中的估计提供了严格的符号界限,其中可能存在结果和中介关系的残余混淆,并且所有测量变量都是二元的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biostatistics
Biostatistics 生物-数学与计算生物学
CiteScore
5.10
自引率
4.80%
发文量
45
审稿时长
6-12 weeks
期刊介绍: Among the important scientific developments of the 20th century is the explosive growth in statistical reasoning and methods for application to studies of human health. Examples include developments in likelihood methods for inference, epidemiologic statistics, clinical trials, survival analysis, and statistical genetics. Substantive problems in public health and biomedical research have fueled the development of statistical methods, which in turn have improved our ability to draw valid inferences from data. The objective of Biostatistics is to advance statistical science and its application to problems of human health and disease, with the ultimate goal of advancing the public''s health.
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