In this paper we present a framework to do estimation in a structural Cox model when there may be unobserved confounding. The model is phrased in terms of a selection bias function and a baseline model that describes how covariates affect the survival time in a scenario without exposure. In this way model congeniality is ensured. The method uses an instrumental variable. Interestingly, the formulated model turns out to have similarities to the so-called Cox–Aalen survival model for the observed data. We exploit this to enhance estimation of the unknown parameters. This also allows us to derive large sample properties of the proposed estimator.

中文翻译：

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IV设置下同质或异质选择下的因果比例风险估计量。

在本文中，我们提出了一个框架，用于在结构Cox模型中进行可能无法观察到的混淆时进行估计。该模型用选择偏倚函数和基准模型来表述，该基准模型描述了协变量如何在没有暴露的情况下影响生存时间。通过这种方式，可以确保模型的一致性。该方法使用工具变量。有趣的是，对于观察到的数据，该公式化的模型与所谓的Cox–Aalen生存模型具有相似性。我们利用它来增强对未知参数的估计。这也使我们能够推导拟议估计量的大样本属性。