Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Int J Epidemiol. 2010 Feb;39(1):107-17. doi: 10.1093/ije/dyp332. Epub 2009 Nov 30.
The impact of unmeasured confounders on causal associations can be studied by means of sensitivity analyses. Although several sensitivity analyses are available, these are used infrequently. This article is intended as a tutorial on sensitivity analyses, in which we discuss three methods to conduct sensitivity analysis.
Each method is based on assumed associations between confounder and exposure, confounder and outcome and the prevalence of the confounder in the population at large. In the first method an unmeasured confounder is simulated and subsequently adjusted. The other two methods are analytical methods, in which either the (adjusted) effect estimate is multiplied with a factor based on assumed confounder characteristics, or the (adjusted) risks for the outcome among exposed and unexposed subjects are adjusted by such a factor. These methods are illustrated with a clinical example on influenza vaccine effectiveness.
When applied to a dataset constructed to assess the effect of influenza vaccination on mortality, the three reviewed methods provided similar results. After adjustment for observed confounders, influenza vaccination reduced mortality by 42% [odds ratio (OR) 0.58, 95% confidence interval (CI) 0.46-0.73]. To arrive at a 95% CI including one requires a very common confounder (40% prevalence) with strong associations with both vaccination status and mortality, respectively OR < or =0.3 and OR > or =3.0 (OR 0.79, 95% CI 0.62-1.00).
In every non-randomized study on causal associations the robustness of the results with respect to unmeasured confounding can, and should, be assessed using sensitivity analyses.
未测量的混杂因素对因果关系的影响可以通过敏感性分析来研究。虽然有几种敏感性分析方法，但这些方法很少被使用。本文旨在介绍敏感性分析，讨论三种进行敏感性分析的方法。
每种方法都基于混杂因素与暴露因素、混杂因素与结局因素以及混杂因素在总体人群中的流行率之间的假设关联。第一种方法模拟一个未测量的混杂因素，并对其进行调整。另外两种方法是分析方法，其中一种方法是根据假设的混杂因素特征，将（调整后的）效应估计值乘以一个因子，另一种方法是根据假设的混杂因素特征，调整暴露组和非暴露组的（调整后的）结局风险。本文以流感疫苗有效性的临床实例说明了这些方法。
将这三种方法应用于评估流感疫苗对死亡率的影响的数据集，结果相似。在调整了观察到的混杂因素后，流感疫苗使死亡率降低了 42%[比值比（OR）0.58，95%置信区间（CI）0.46-0.73]。要得到一个包含 95%置信区间的结果，需要一个非常常见的混杂因素（40%的流行率），其与疫苗接种状态和死亡率分别有很强的关联，OR 小于等于 0.3 和 OR 大于等于 3.0（OR 0.79，95% CI 0.62-1.00）。
在每一项非随机的因果关系研究中，都应该使用敏感性分析来评估未测量混杂因素对结果的稳健性。
