Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Clinical Epidemiology & Biostatistics Unit and Melbourne Children's Trial Centre, Murdoch Childrens Research Institute, Melbourne, Victoria, Australia.
Int J Epidemiol. 2017 Aug 1;46(4):1303-1311. doi: 10.1093/ije/dyx023.
A critical assumption of causal inference is that of no unmeasured confounding: for estimated exposure effects to have valid causal interpretations, a sufficient set of predictors of exposure and outcome must be adequately measured and correctly included in the respective inference model(s). In an observational study setting, this assumption will often be unsatisfied, and the potential impact of unmeasured confounding on effect estimates should be investigated. The confounding function approach allows the impact of unmeasured confounding on estimates to be assessed, where unmeasured confounding may be due to unmeasured confounders and/or biases such as collider bias or information bias. Although this approach is easy to implement and pertains to the sum of all bias, its use has not been widespread, and discussion has typically been limited to continuous outcomes. In this paper, we consider confounding functions for use with binary outcomes and illustrate the approach with an example. We note that confounding function choice encodes assumptions about effect modification: some choices encode the belief that the true causal effect differs across exposure groups, whereas others imply that any difference between the true causal parameter and the estimate is entirely due to imbalanced risks between exposure groups. The confounding function approach is a useful method for assessing the impact of unmeasured confounding, in particular when alternative approaches, e.g. external adjustment or instrumental variable approaches, cannot be applied. We provide Stata and R code for the implementation of this approach when the causal estimand of interest is an odds or risk ratio.
为了使暴露效应的估计具有有效的因果解释，必须充分测量和正确包含暴露和结果的足够预测因子，并将其包含在相应的推断模型中。在观察性研究中，这一假设通常无法满足，因此应该调查未测量混杂对效应估计的潜在影响。混杂函数方法可以评估未测量混杂对估计的影响，其中未测量混杂可能是由于未测量的混杂因素和/或偏差引起的，如共发偏倚或信息偏倚。尽管这种方法易于实现且适用于所有偏差的总和，但它的使用并不广泛，讨论通常仅限于连续结果。在本文中，我们考虑了用于二项结果的混杂函数，并通过一个示例说明了该方法。我们注意到，混杂函数的选择编码了关于效应修正的假设：有些选择编码了这样一种信念，即真实的因果效应在暴露组之间存在差异，而其他选择则暗示真实的因果参数与估计值之间的任何差异完全归因于暴露组之间风险的不平衡。混杂函数方法是一种评估未测量混杂影响的有用方法，特别是在无法应用替代方法（例如外部调整或工具变量方法）的情况下。当感兴趣的因果估计量是优势比或风险比时，我们提供了用于实现这种方法的 Stata 和 R 代码。
