Department of Preventive Medicine and Public Health, University of Santiago de Compostela, c/ San Francisco s/n, 15786, Santiago de Compostela, A Coruña, Spain.
Health Research Institute of Santiago de Compostela (Instituto de Investigación Sanitaria de Santiago de Compostela - IDIS), Clinical University Hospital of Santiago de Compostela, 15706, Santiago de Compostela, Spain.
BMC Med Res Methodol. 2019 Mar 11;19(1):53. doi: 10.1186/s12874-019-0695-y.
The availability of clinical and therapeutic data drawn from medical records and administrative databases has entailed new opportunities for clinical and epidemiologic research. However, these databases present inherent limitations which may render them prone to new biases. We aimed to conduct a structured review of biases specific to observational clinical studies based on secondary databases, and to propose strategies for the mitigation of those biases.
Scoping review of the scientific literature published during the period 2000-2018 through an automated search of MEDLINE, EMBASE and Web of Science, supplemented with manually cross-checking of reference lists. We included opinion essays, methodological reviews, analyses or simulation studies, as well as letters to the editor or retractions, the principal objective of which was to highlight the existence of some type of bias in pharmacoepidemiologic studies using secondary databases.
A total of 117 articles were included. An increasing trend in the number of publications concerning the potential limitations of secondary databases was observed over time and across medical research disciplines. Confounding was the most reported category of bias (63.2% of articles), followed by selection and measurement biases (47.0% and 46.2% respectively). Confounding by indication (32.5%), unmeasured/residual confounding (28.2%), outcome misclassification (28.2%) and "immortal time" bias (25.6%) were the subcategories most frequently mentioned.
Suboptimal use of secondary databases in pharmacoepidemiologic studies has introduced biases in the studies, which may have led to erroneous conclusions. Methods to mitigate biases are available and must be considered in the design, analysis and interpretation phases of studies using these data sources.
从病历和管理数据库中获取的临床和治疗数据为临床和流行病学研究带来了新的机会。然而,这些数据库存在固有局限性,可能导致新的偏倚。我们旨在对基于二级数据库的观察性临床研究的特定偏倚进行系统评价,并提出减轻这些偏倚的策略。
通过自动搜索 MEDLINE、EMBASE 和 Web of Science,对 2000 年至 2018 年期间发表的科学文献进行了范围审查,并通过手动交叉检查参考文献进行补充。我们纳入了意见文章、方法学综述、分析或模拟研究,以及给编辑的信或撤回信,主要目的是强调使用二级数据库进行药物流行病学研究存在某种类型的偏倚。
共纳入 117 篇文章。随着时间的推移和医学研究学科的发展,越来越多的出版物关注二级数据库的潜在局限性。混杂是报告最多的偏倚类别(63.2%的文章),其次是选择和测量偏倚(分别为 47.0%和 46.2%)。最常提到的亚类别包括指示性混杂(32.5%)、未测量/残留混杂(28.2%)、结局错误分类(28.2%)和“不朽时间”偏倚(25.6%)。
药物流行病学研究中二级数据库的使用不当导致研究存在偏倚,可能导致错误的结论。在使用这些数据源进行研究的设计、分析和解释阶段,必须考虑减轻偏倚的方法。