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arXiv 2507.03464stat.ME
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同胞比较的因果解释及其与交叉设计的关系

Causal interpretation of the sibling comparison and its relation to the cross-over design

Simon Bang Kristensen, Christian Bressen Pipper, Jacob von Bornemann Hjelmborg

AI总结:

本文用交叉设计刻画同胞比较,揭示其因果目标面临的暴露不一致配对和反向暴露干预两大挑战,并提出基于未观测混杂结构估计的加权方法以评估匹配分析假设并扩展至消除选择偏倚。

摘要:

采用同胞比较设计的直观动机是为了调整家庭内部恒定的混杂因素。这类混杂可能由原本难以测量的变量引起,例如与遗传、环境和成长经历有关的因素。近期的方法学研究表明,尽管同胞比较设计具有直观吸引力,但即便在恒定混杂的情况下,常规采用的分析并不对应一个定义明确的因果目标。一个主要挑战在于,该分析将以暴露状态不一致的配对子人群为目标。在同胞的暴露对目标同胞的结局存在影响时,还存在第二个挑战,即该效应对应于一种总是使同胞接受与目标同胞相反暴露的干预。我们用交叉设计来刻画同胞比较。在利用这一刻画为以适当的因果参数为目标建立更自然的条件之前,先讨论感兴趣的估计量。我们将同胞比较的上述挑战用交叉试验研究者所面临的挑战来表述:为了以适当的估计量为目标,必须能够论证不存在某种类型的延续效应以及不存在试验与处理的交互作用,从而证明前一种研究设计完整地模拟了后一种设计,包括其所有缺陷。我们探索使用加权来抵消此类交互作用的影响,并以其他估计量为目标。这些权重依赖于对未观测混杂结构的估计。通过模拟和一个示例分析,我们说明了该方法在评估匹配分析假设有效性方面的潜在用途。我们简要讨论了将该加权程序扩展为基于人群水平参考样本数据来消除选择偏倚。

英文摘要:

The intuitive motivation for employing a sibling comparison design is to adjust for confounding that is constant within families. Such confounding can be caused by variables that otherwise might prove difficult to measure, for example factors relating to genetics, environment, and upbringing. Recent methodological investigations have shown that despite its intuitive appeal, the conventionally employed analysis does not relate to a well-defined causal target, even in the case of constant confounding. A main challenge is that the analysis will target the subpopulation of exposure discordant pairs. In the presence of an effect of the cosibling's exposure on the sibling's outcome, there is a second challenge, namely that the effect corresponds to an intervention that always exposes the cosibling to the opposite exposure from the sibling. We characterise the sibling comparison in terms of the cross-over design. Estimands of interest are discussed before using this characterisation to establish more natural conditions for targeting an appropriate causal parameter. We cast the above-mentioned challenges of the sibling comparison in terms of those facing the cross-over trialist: in order to target an appropriate estimand one must be able to argue the absence of a certain type of carry-over effect as well as absence of trial-by-treatment interaction, thus establishing that the former study design emulates the latter warts and all. We explore weighting to counter the effects of such interactions and to target other estimands. The weights rely on estimates of the unobserved confounding structure. Through simulations and an example analysis, we illustrate its potential usefulness to assess the validity of the assumptions of the matched analysis. We briefly discuss an extension of the weighting procedure to remove selection bias based on data from a population-level reference sample.

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