Shi Xu, Miao Wang, Tchetgen Eric Tchetgen
Department of Biostatistics, University of Michigan, Ann Arbor, USA.
Department of Probability and Statistics, Peking University, Beijing, China.
Curr Epidemiol Rep. 2020 Dec;7(4):190-202. doi: 10.1007/s40471-020-00243-4. Epub 2020 Oct 15.
Negative controls are a powerful tool to detect and adjust for bias in epidemiological research. This paper introduces negative controls to a broader audience and provides guidance on principled design and causal analysis based on a formal negative control framework.
We review and summarize causal and statistical assumptions, practical strategies, and validation criteria that can be combined with subject-matter knowledge to perform negative control analyses. We also review existing statistical methodologies for the detection, reduction, and correction of confounding bias, and briefly discuss recent advances towards nonparametric identification of causal effects in a double-negative control design.
There is great potential for valid and accurate causal inference leveraging contemporary healthcare data in which negative controls are routinely available. Design and analysis of observational data leveraging negative controls is an area of growing interest in health and social sciences. Despite these developments, further effort is needed to disseminate these novel methods to ensure they are adopted by practicing epidemiologists.
阴性对照是检测和调整流行病学研究中偏倚的有力工具。本文向更广泛的受众介绍阴性对照，并基于正式的阴性对照框架提供有关原则性设计和因果分析的指导。
我们回顾并总结了因果和统计假设、实际策略以及验证标准，这些可与主题知识相结合以进行阴性对照分析。我们还回顾了用于检测、减少和校正混杂偏倚的现有统计方法，并简要讨论了在双阴性对照设计中因果效应非参数识别方面的最新进展。
利用常规可得阴性对照的当代医疗数据进行有效且准确的因果推断具有巨大潜力。利用阴性对照对观察性数据进行设计和分析是健康与社会科学领域中日益受到关注的一个领域。尽管有这些进展，但仍需要进一步努力传播这些新方法，以确保它们被执业流行病学家采用。
