Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Am J Epidemiol. 2019 Jul 1;188(7):1355-1360. doi: 10.1093/aje/kwz060.
In the absence of strong assumptions (e.g., exchangeability), only bounds for causal effects can be identified. Here we describe bounds for the risk difference for an effect of a binary exposure on a binary outcome in 4 common study settings: observational studies and randomized studies, each with and without simple random selection from the target population. Through these scenarios, we introduce randomizations for selection and treatment, and the widths of the bounds are narrowed from 2 (the width of the range of the risk difference) to 0 (point identification). We then assess the strength of the assumptions of exchangeability for internal and external validity by comparing their contributions to the widths of the bounds in the setting of an observational study without random selection from the target population. We find that when less than two-thirds of the target population is selected into the study, the assumption of exchangeability for external validity of the risk difference is stronger than that for internal validity. The relative strength of these assumptions should be considered when designing, analyzing, and interpreting observational studies and will aid in determining the best methods for estimating the causal effects of interest.
在没有严格假设(例如可交换性)的情况下,只能确定因果效应的界。在这里,我们描述了在 4 种常见的研究设置中,二元暴露对二元结局的效应的风险差的界:观察性研究和随机研究,每种研究都有且没有简单随机选择目标人群。通过这些场景,我们引入了选择和治疗的随机化,并且界的宽度从 2(风险差的范围宽度)缩小到 0(点识别)。然后,我们通过比较它们对无目标人群随机选择的观察性研究中界宽度的贡献,评估了内部和外部有效性的可交换性假设的强度。我们发现,当不到三分之二的目标人群被纳入研究时,风险差的外部有效性的可交换性假设比内部有效性的假设更强。在设计、分析和解释观察性研究时,应考虑这些假设的相对强度,并有助于确定估计感兴趣的因果效应的最佳方法。