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


来自大型人群数据库的观察性研究可能会受到未测量的混杂和时间相关偏倚的影响，例如不朽的时间偏倚。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。这些方法学方法在多大程度上充分解决了任何特定研究的偏倚，可能很难衡量。最近，已确定将阳性和阴性对照纳入作为评估残余混杂和/或时间相关偏倚影响的一种手段。本评论的目的是描述阳性和阴性对照在观察性研究中的作用。我们提供将控制措施纳入关键评估和观察性研究项目的建议。