Confounders are difficult to avoid in studies on observational comparative effectiveness. It is often unclear whether the confounders have been completely eliminated after controlling the measured or unmeasured potential confounding effects or if sensitivity analysis is needed when using the specific statistical methods, under given circumstances. This manuscript summarizes and evaluates the confounding sensitivity analysis methods. Based on different studies, sensitivity analyses need to use different approaches. The traditional sensitivity analysis can be applied for the measured confounders. Currently, the relatively systematic sensitivity analyses for unmeasured confounders would include confounding function, bounding factor and propensity score calibration. Additionally, more investigations are associated with Monte Carlo and Bayesian sensitivity analysis. Reliability of the research conclusion thus may largely be improved when the sensitivity analysis results are consistent with the main analysis.
观察性疗效比较研究中混杂在所难免，在利用一些统计分析方法对已测量或未测量混杂因素加以控制后，是否消除了混杂的影响不得而知，此时需进行敏感性分析。本文介绍混杂因素处理中的敏感性分析方法。基于不同的研究，敏感性分析思路各不相同，对于已测量混杂因素可采用传统的敏感性分析方法，对于未测量混杂因素目前理论相对系统的方法主要有混杂函数法、边界因子法和倾向性评分校正法，另外Monte Carlo敏感性分析和Bayes敏感性分析也是近年来备受热议的方法。当敏感性分析结果与主要分析结果一致时，无疑提高了研究结论的可靠性。.
Keywords: Adjustment; Confounder; Observational comparative effectiveness research; Sensitivity analysis.