Observational research from large population databases may be affected by unmeasured confounding and time-related biases, such as immortal time bias. Modern causal inference practice applies propensity score-based methods, new-user designs, and other strategies to mitigate bias. The degree to which these methodologic approaches adequately address bias for any particular study may be difficult to measure. Recently, the incorporation of positive and negative controls has been identified as a means to assess for the impacts of residual confounding and/or time-related biases. The objective of this commentary is to describe the role of positive and negative controls in observational research. We offer recommendations for incorporating controls into critical appraisal and observational research projects.

中文翻译：

---

风湿病学研究中的阳性和阴性对照


来自大型群体数据库的观察性研究可能会受到无法测量的混杂因素和与时间相关的偏差的影响，例如不朽的时间偏差。现代因果推理实践应用基于倾向评分的方法、新用户设计和其他策略来减轻偏见。这些方法论在多大程度上充分解决任何特定研究的偏见可能很难衡量。最近，阳性和阴性对照的结合已被确定为评估残留混杂和/或时间相关偏差影响的一种方法。本评论的目的是描述观察性研究中阳性和阴性对照的作用。我们提供将控制纳入关键评估和观察研究项目的建议。