Identifying causal treatment (or exposure) effects in observational studies requires the data to satisfy the unconfoundedness assumption which is not testable using the observed data. With sensitivity analysis, one can determine how the conclusions might change if assumptions are violated to a certain degree. In this paper, we propose a new technique for sensitivity analysis applicable to clusters observational data with a normally distributed or binary outcome. The proposed methods aim to assess the robustness of estimated treatment effects in a single study as well as in multiple studies, i.e., meta-analysis, against unmeasured confounders. Simulations with various underlying scenarios were conducted to assess the performance of our methods. Unlike other existing sensitivity analysis methods, our methods have no restrictive assumptions on the number of unmeasured confounders or on the relationship between measured and unmeasured confounders, and do not exclude possible interactions between measured confounders and the treatment. Our methods are easy to implement using standard statistical software packages.

翻译：识别观察性研究中的因果处理（或暴露）效应需满足不可检验的"无混杂假定"。通过敏感性分析，可评估当假定被违反至特定程度时结论的变化趋势。本文提出一种适用于正态分布或二分类结果变量的聚类观测数据敏感性分析新技术。所提方法旨在评估单研究及多研究（即元分析）中估计处理效应对未测量混杂因素的稳健性。我们通过模拟不同潜在场景检验方法性能。相较于现有其他敏感性分析方法，本方法不对未测量混杂因素数量、测量与未测量混杂因素关系设定限制性假设，同时允许测量混杂因素与处理间存在交互作用。本方法易于通过标准统计软件包实现。