Gaucher Louis, Sabatier Pierre, Katsahian Sandrine, Jannot Anne-Sophie
HeKA INSERM, INRIA Paris, Centre de Recherche des Cordeliers Paris, Université Paris Cité, Paris, France.
Clinical Research Unit, Hôpital Européen Georges Pompidou, APHP, Paris, France.
J Clin Epidemiol. 2023 Oct;162:127-134. doi: 10.1016/j.jclinepi.2023.08.010. Epub 2023 Aug 31.
The purpose of this study was to systematically review the statistical methods used in pharmacovigilance studies without a priori hypotheses.
A systematic review was performed on studies published in the MEDLINE database between 2012 and 2021. The included studies were analyzed for database name and type, statistical methods, anatomical therapeutic chemical class for the studied drug(s), and SOC MedDRA classification for the studied adverse drug reaction.
Ninety-two studies were included, with pharmacovigilance databases being the most used type. Disproportionality analysis using frequentist or Bayesian methods was the most common statistical method employed. The most studied drug classes were anti-infectives, nervous system drugs, and antineoplastics and immunomodulators. However, no common procedure was implemented to correct for multiple testing.
This review highlights the limited number of statistical methods employed for pharmacovigilance studies without a priori hypotheses, with no established consensus-based method and a lack of interest in multiple testing correction. The establishment of guidelines is recommended to improve the performance of such studies.
本研究旨在系统回顾无先验假设的药物警戒研究中所使用的统计方法。
对2012年至2021年期间发表在MEDLINE数据库中的研究进行了系统回顾。对纳入研究的数据库名称和类型、统计方法、所研究药物的解剖治疗化学分类以及所研究药物不良反应的系统器官分类（SOC MedDRA）进行了分析。
纳入了92项研究，药物警戒数据库是使用最多的类型。使用频率学派或贝叶斯方法的不成比例分析是最常用的统计方法。研究最多的药物类别是抗感染药、神经系统药物以及抗肿瘤药和免疫调节剂。然而，没有实施通用程序来校正多重检验。
本综述强调了无先验假设的药物警戒研究中所采用统计方法的数量有限，没有基于共识的既定方法，且对多重检验校正缺乏关注。建议制定指南以提高此类研究的效能。
