McCandless Lawrence C, Gustafson Paul, Levy Adrian R
Department of Statistics, University of British Columbia, Vancouver BC, V6T 1Z2, Canada.
J Clin Epidemiol. 2008 Mar;61(3):247-55. doi: 10.1016/j.jclinepi.2007.05.006. Epub 2007 Oct 15.
In the analysis of observational data, the argument is sometimes made that if adjustment for measured confounders induces little change in the treatment-outcome association, then there is less concern about the extent to which the association is driven by unmeasured confounding. We quantify this reasoning using Bayesian sensitivity analysis (BSA) for unmeasured confounding. Using hierarchical models, the confounding effect of a binary unmeasured variable is modeled as arising from the same distribution as that of measured confounders. Our objective is to investigate the performance of the method compared to sensitivity analysis, which assumes that there is no relationship between measured and unmeasured confounders.
We apply the method in an observational study of the effectiveness of beta-blocker therapy in heart failure patients.
BSA for unmeasured confounding using hierarchical prior distributions yields an odds ratio (OR) of 0.72, 95% credible interval (CrI): 0.56, 0.93 for the association between beta-blockers and mortality, whereas using independent priors yields OR=0.72, 95% CrI: 0.45, 1.15.
If the confounding effect of a binary unmeasured variable is similar to that of measured confounders, then conventional sensitivity analysis may give results that overstate the uncertainty about bias.
在观察性数据分析中，有时会有人认为，如果对已测量的混杂因素进行调整后，治疗与结局之间的关联变化不大，那么就无需过多担心该关联受未测量混杂因素影响的程度。我们使用贝叶斯敏感性分析（BSA）对未测量的混杂因素进行量化。通过分层模型，将二元未测量变量的混杂效应建模为与已测量混杂因素来自相同的分布。我们的目的是研究该方法与敏感性分析相比的性能，敏感性分析假定已测量和未测量的混杂因素之间不存在关系。
我们将该方法应用于一项关于β受体阻滞剂治疗心力衰竭患者有效性的观察性研究中。
使用分层先验分布对未测量的混杂因素进行贝叶斯敏感性分析，β受体阻滞剂与死亡率之间的关联得出的比值比（OR）为0.72，95%可信区间（CrI）：0.56，0.93；而使用独立先验分布得出的OR = 0.72，95% CrI：0.45，1.15。
如果二元未测量变量的混杂效应与已测量混杂因素的混杂效应相似，那么传统的敏感性分析可能会给出高估偏差不确定性的结果。
