Unmeasured confounding is an important threat to the validity of observational studies. A common way to deal with unmeasured confounding is to compute bounds for the causal effect of interest, that is, a range of values that is guaranteed to include the true effect, given the observed data. Recently, bounds have been proposed that are based on sensitivity parameters, which quantify the degree of unmeasured confounding on the risk ratio scale. These bounds can be used to compute an E -value, that is, the degree of confounding required to explain away an observed association, on the risk ratio scale. We complement and extend this previous work by deriving analogous bounds, based on sensitivity parameters on the risk difference scale. We show that our bounds can also be used to compute an E -value, on the risk difference scale. We compare our novel bounds with previous bounds through a real data example and a simulation study.

中文翻译：

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基于风险差异量表上的敏感性参数的因果效应的新界限

不可测量的混杂因素是对观察性研究有效性的重要威胁。处理未测量的混杂的一种常见方法是计算感兴趣的因果效应的界限，即在给定观察数据的情况下，保证包含真实效应的一系列值。最近，已经提出了基于敏感性参数的界限，这些界限量化了风险比量表上未测量的混杂程度。这些界限可用于计算 E 值，即在风险比量表上解释观察到的关联所需的混杂程度。我们通过基于风险差异量表的敏感性参数推导类似的界限来补充和扩展这项先前的工作。我们表明，我们的界限也可用于在风险差异尺度上计算 E 值。