碰撞偏差的一个核心定理。

IF 1.2 4区 数学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Doron J Shahar, Eyal Shahar
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引用次数: 10

摘要

对两个变量的共同结果进行调节可以改变这些变量之间的关联,在估计效果时可能会增加偏倚成分。特别是,如果两个原因在一定程度上是独立的,它们可能在其共同影响的层次上是依赖的。然而,对这一现象的解释并没有明确说明依赖将在何时产生,而且在很大程度上是非正式的。我们证明,两个边际独立的原因,当且仅当它们在概率比尺度上,对结果变量的值改变彼此的影响时,将依赖于它们共同结果的特定阶层。使用我们的结果,我们还可以证明这些原因“几乎肯定”在至少一个结果层中是依赖的:依赖性必须在二元结果的一个层中产生,而独立性可以在三元结果的每个层中保持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Theorem at the Core of Colliding Bias.

Conditioning on a shared outcome of two variables can alter the association between these variables, possibly adding a bias component when estimating effects. In particular, if two causes are marginally independent, they might be dependent in strata of their common effect. Explanations of the phenomenon, however, do not explicitly state when dependence will be created and have been largely informal. We prove that two, marginally independent, causes will be dependent in a particular stratum of their shared outcome if and only if they modify each other's effects, on a probability ratio scale, on that value of the outcome variable. Using our result, we also qualify the claim that such causes will "almost certainly" be dependent in at least one stratum of the outcome: dependence must be created in one stratum of a binary outcome, and independence can be maintained in every stratum of a trinary outcome.

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来源期刊
International Journal of Biostatistics
International Journal of Biostatistics MATHEMATICAL & COMPUTATIONAL BIOLOGY-STATISTICS & PROBABILITY
CiteScore
2.10
自引率
8.30%
发文量
28
审稿时长
>12 weeks
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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