Causal conclusions from observational studies may be sensitive to unmeasured confounding. In such cases, a sensitivity analysis is often conducted, which tries to infer the minimum amount of hidden biases or the minimum strength of unmeasured confounding needed in order to explain away the observed association between treatment and outcome. If the needed bias is large, then the treatment is likely to have significant effects. The Rosenbaum sensitivity analysis is a modern approach for conducting sensitivity analysis in matched observational studies. It investigates what magnitude the maximum of hidden biases from all matched sets needs to be in order to explain away the observed association. However, such a sensitivity analysis can be overly conservative and pessimistic, especially when investigators suspect that some matched sets may have exceptionally large hidden biases. In this paper, we generalize Rosenbaum's framework to conduct sensitivity analysis on quantiles of hidden biases from all matched sets, which are more robust than the maximum. Moreover, the proposed sensitivity analysis is simultaneously valid across all quantiles of hidden biases and is thus a free lunch added to the conventional sensitivity analysis. The proposed approach works for general outcomes, general matched studies and general test statistics. In addition, we demonstrate that the proposed sensitivity analysis also works for bounded null hypotheses when the test statistic satisfies certain properties. An R package implementing the proposed approach is available online.

翻译：观察性研究中的因果结论可能对未测量的混杂因素敏感。在这种情况下，通常需要进行敏感性分析，以推断解释处理与结果之间观察到的关联所需的最小隐藏偏倚量或未测量混杂的最小强度。若所需偏倚较大，则处理很可能具有显著效应。Rosenbaum敏感性分析是匹配观察性研究中执行敏感性分析的现代方法，它探究所有匹配集中隐藏偏倚的最大值需要达到何种程度才能解释观察到的关联。然而，此类敏感性分析可能过于保守和悲观，尤其当研究者怀疑某些匹配集可能存在异常大的隐藏偏倚时。本文推广了Rosenbaum的框架，对所有匹配集中隐藏偏倚的分位数进行敏感性分析，这些分位数比最大值更具稳健性。此外，所提出的敏感性分析在隐藏偏倚的所有分位数上同时有效，因此是对传统敏感性分析的免费补充。所提方法适用于一般结果、一般匹配研究和一般检验统计量。另外，我们证明了当检验统计量满足特定性质时，所提出的敏感性分析也适用于有界零假设。实现该方法的R包已在线发布。