Am J Epidemiol. 2024 Feb 5;193(3):426-453. doi: 10.1093/aje/kwad201.
Uses of real-world data in drug safety and effectiveness studies are often challenged by various sources of bias. We undertook a systematic search of the published literature through September 2020 to evaluate the state of use and utility of negative controls to address bias in pharmacoepidemiologic studies. Two reviewers independently evaluated study eligibility and abstracted data. Our search identified 184 eligible studies for inclusion. Cohort studies (115, 63%) and administrative data (114, 62%) were, respectively, the most common study design and data type used. Most studies used negative control outcomes (91, 50%), and for most studies the target source of bias was unmeasured confounding (93, 51%). We identified 4 utility domains of negative controls: 1) bias detection (149, 81%), 2) bias correction (16, 9%), 3) P-value calibration (8, 4%), and 4) performance assessment of different methods used in drug safety studies (31, 17%). The most popular methodologies used were the 95% confidence interval and P-value calibration. In addition, we identified 2 reference sets with structured steps to check the causality assumption of the negative control. While negative controls are powerful tools in bias detection, we found many studies lacked checking the underlying assumptions. This article is part of a Special Collection on Pharmacoepidemiology.
真实世界数据在药物安全性和有效性研究中的应用常受到各种偏倚源的挑战。我们通过系统检索截至 2020 年 9 月已发表的文献,评估了负向对照在药物流行病学研究中用于处理偏倚的使用情况和实用性。两名审查员独立评估了研究的合格性并提取了数据。我们的检索确定了 184 项符合纳入标准的研究。队列研究(115 项,63%)和行政数据(114 项,62%)分别是最常用的研究设计和数据类型。大多数研究使用了负向对照结局(91 项,50%),对于大多数研究,目标偏倚源是未测量的混杂(93 项,51%)。我们确定了负向对照的 4 个实用领域:1)偏倚检测(149 项,81%),2)偏倚校正(16 项,9%),3)P 值校准(8 项,4%),4)药物安全性研究中不同方法的性能评估(31 项,17%)。最常用的方法学是 95%置信区间和 P 值校准。此外,我们还确定了 2 个具有结构化步骤的参考集,以检查负向对照的因果关系假设。虽然负向对照是检测偏倚的有力工具,但我们发现许多研究缺乏对潜在假设的检查。本文是药物流行病学特刊的一部分。