Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, The Netherlands.
Department of Clinical Pharmacy, University Medical Centre Utrecht, Utrecht, The Netherlands.
Pharmacoepidemiol Drug Saf. 2021 Jul;30(7):819-826. doi: 10.1002/pds.5245. Epub 2021 May 7.
Pharmacoepidemiologic multi-database studies (MDBS) provide opportunities to better evaluate the safety and effectiveness of medicines. However, the issue of missing data is often exacerbated in MDBS, potentially resulting in bias and precision loss. We sought to measure how missing data are being recorded and addressed in pharmacoepidemiologic MDBS.
We conducted a systematic literature search in PubMed for pharmacoepidemiologic MDBS published between 1st January 2018 and 31st December 2019. Included studies were those that used ≥2 distinct databases to assess the same safety/effectiveness outcome associated with a drug exposure. Outcome variables extracted from the studies included strategies to execute a MDBS, reporting of missing data (type, bias evaluation) and the methods used to account for missing data.
Two thousand seven hundred and twenty-six articles were identified, and 62 studies were included: using data from either North America (56%), Europe (31%), multiple regions (11%) or East-Asia (2%). Thirty-five (56%) articles reported missing data: 11 of these studies reported that this could have introduced bias and 19 studies reported a method to address missing data. Thirteen (68%) carried out a complete case analysis, 2 (11%) applied multiple imputation, 2 (11%) used both methods, 1 (5%) used mean imputation and 1 (5%) substituted information from a similar variable.
Just over half of the recent pharmacoepidemiologic MDBS reported missing data and two-thirds of these studies reported how they accounted for it. We should increase our vigilance for database completeness in MDBS by reporting and addressing the missing data that could introduce bias.
药物流行病学多数据库研究（MDBS）为更好地评估药物的安全性和有效性提供了机会。然而，在 MDBS 中，数据缺失问题经常加剧，可能导致偏差和精度损失。我们旨在衡量药物流行病学 MDBS 中缺失数据的记录和处理方式。
我们在 PubMed 中进行了一项系统文献检索，检索了 2018 年 1 月 1 日至 2019 年 12 月 31 日期间发表的药物流行病学 MDBS 研究。纳入的研究是使用≥2 个不同数据库评估与药物暴露相关的相同安全性/有效性结局的研究。从研究中提取的结果变量包括执行 MDBS 的策略、缺失数据的报告（类型、偏差评估）以及用于处理缺失数据的方法。
共确定了 2726 篇文章，其中 62 篇符合纳入标准：研究数据来自北美（56%）、欧洲（31%）、多个地区（11%）或东亚（2%）。35 篇（56%）文章报告了缺失数据：其中 11 篇文章报道称这可能引入了偏差，19 篇文章报道了处理缺失数据的方法。13 篇（68%）进行了完全病例分析，2 篇（11%）采用了多重插补，2 篇（11%）同时使用了这两种方法，1 篇（5%）采用了均值插补，1 篇（5%）使用了类似变量的替代信息。
最近的药物流行病学 MDBS 中只有一半以上报告了缺失数据，其中三分之二的研究报告了如何处理这些数据。我们应该通过报告和处理可能引入偏差的缺失数据，提高对 MDBS 中数据库完整性的警惕性。
