Inferring the causal effect of a non-randomly assigned exposure on an outcome requires adjusting for common causes of the exposure and outcome to avoid biased conclusions. Notwithstanding the efforts investigators routinely make to measure and adjust for such common causes (or confounders), some confounders typically remain unmeasured, raising the prospect of biased inference in observational studies. Therefore, it is crucial that investigators can practically assess their substantive conclusions' relative (in)sensitivity to potential unmeasured confounding. In this article, we propose a sensitivity analysis strategy that is informed by the stability of the exposure effect over different, well-chosen subsets of the measured confounders. The proposal entails first approximating the process for recording confounders to learn about how the effect is potentially affected by varying amounts of unmeasured confounding, then extrapolating to the effect had hypothetical unmeasured confounders been additionally adjusted for. A large set of measured confounders can thus be exploited to provide insight into the likely presence of unmeasured confounding bias, albeit under an assumption about how data on the confounders are recorded. The proposal's ability to reveal the true effect and ensure valid inference after extrapolation is empirically compared with existing methods using simulation studies. We demonstrate the procedure using two different publicly available datasets commonly used for causal inference.
The Impact of Unmeasured Within- and Between-Cluster Confounding on the Bias of Effect Estimators from Fixed Effect, Mixed effect and Instrumental Variable Models
Adjustment for Unmeasured Spatial Confounding in Settings of Continuous Exposure Conditional on the Binary Exposure Status: Conditional Generalized Propensity Score-Based Spatial Matching
Bayesian Sensitivity Analysis for Causal Estimation with Time-varying Unmeasured Confounding
Dalla Lana School of Public Health, University of Toronto（多伦多大学达拉·兰纳公共卫生学院）；Public Health Ontario（安大略省公共卫生局）；Rutgers School of Public Health, Rutgers University（罗格斯大学罗格斯公共卫生学院）；Division of Gastroenterology, Hepatology Nutrition, The Hospital for Sick Children（儿童医院胃肠病学、肝病营养科）；University of Utah and Intermountain Healthcare Primary Children’s Hospital（犹他大学和山脊医疗初级儿童医院）；Institute of Health Policy, Management and Evaluation, University of Toronto（多伦多大学卫生政策、管理与评估研究所）
Real Effect or Bias? Best Practices for Evaluating the Robustness of Real-World Evidence through Quantitative Sensitivity Analysis for Unmeasured Confounding
Sensitivity Analysis of Treatment Effect to Unmeasured Confounding in Observational Studies with Survival and Competing Risks Outcomes
当使用倾向得分和逆概率加权等方法时，在估计观察性数据的处理效应时，通常假设没有未测量的混杂因素。然而，在许多这样的研究中，由于数据库的限制，收集的混杂因素并不详尽，关键是要检查所得到的估计值对未测量的混杂因素的敏感程度。我们考虑生存和竞争风险数据的这个问题。由于此类数据模型的复杂性，我们采用了Carnegie et al.（2016）的模拟潜在混杂因素方法，该方法为未测量混杂因素的敏感性分析提供了通用工具。更具体地说，我们指定一个敏感性参数来量化未测量的混杂因素与治疗分配之间的关联，另一组参数来量化混杂因素与事件发生时间结局之间的关联。通过改变灵敏度参数的大小，我们使用随机EM和期望最大化算法来估计感兴趣的处理效应。我们证明了我们的方法在模拟数据上的性能，并将其应用于炎症性肠病（IBD）的比较有效性研究。
Mediation analysis with unmeasured confounding between parallel mediators and outcome
School of Mathematical Sciences, Peking University（北京大学数学科学学院）；The Beijing Institute of Brain Disorders, Chinese Institutes for Medical Research, Capital Medical University（首都医科大学中国医学研究院北京脑 disorder 研究所）
A flexible sensitivity analysis approach for unmeasured confounding with multiple treatments and a binary outcome with application to SEER-Medicare lung cancer data
Proximal Mediation Analysis with Unmeasured Treatment-Induced Confounding
Center for Applied Statistics and School of Statistics, Renmin University of China（中国人民大学统计学院与应用统计中心）；Department of Biostatistics & Medical Informatics, University of Wisconsin-Madison（威斯康星大学麦迪逊分校生物统计与医学信息学系）
Partial identification and unmeasured confounding with multiple treatments and multiple outcomes
Harvard T.H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）；Dana-Farber Cancer Institute（达纳法伯癌症研究所）；University of Florida（佛罗里达大学）；University of Central Florida（中佛罗里达大学）；University of California, Santa Barbara（加州大学圣塔芭芭拉分校）
Estimating causal effects of continuous-time dynamic treatments with unmeasured confounders
Key Laboratory of Advanced Theory and Application in Statistics and Data Science -MOE, School of Statistics, East China Normal University（华东师范大学统计学院统计学与数据科学前沿理论及应用教育部重点实验室）
A Doubly Robust Instrumental Variable Approach for Estimating Average Treatment Effects in Time-to-Event Data with Unmeasured Confounding: Application to Real-World Data on ICU Patients with Septic Shock
Semiparametric Mediation Analysis with Separately Observed Mediator and Outcome under Unmeasured Confounding
Department of Biostatistics, University of California, Los Angeles（加州大学洛杉矶分校生物统计系）；Department of Biostatistics, Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院生物统计系）
