Uppsala Monitoring Centre, Box 1051, 751 40, Uppsala, Sweden.
National Institute of Informatics, Tokyo, Japan.
Drug Saf. 2020 May;43(5):479-487. doi: 10.1007/s40264-020-00911-w.
Uncovering safety signals through the collection and assessment of individual case reports remains a core pharmacovigilance activity. Despite the widespread use of disproportionality analysis in signal detection, recommendations are lacking on the minimum size of databases or subsets of databases required to yield robust results.
This study aims to investigate the relationship between database size and robustness of disproportionality analysis, with regards to limiting spurious associations.
Three types of subsets were created from the global database VigiBase: random subsets (500 replicates each of 11 fixed subset sizes between 250 and 100,000 reports), country-specific subsets (all 131 countries available in the original VigiBase extract) and subsets based on the Anatomical Therapeutic Chemical classification. For each subset, a spuriousness rate was computed as the ratio between the number of drug-event combinations highlighted by disproportionality analysis in a permuted version of the subset and the corresponding number in the original subset. In the permuted data, all true reporting associations between drugs and adverse events were broken. Subsets with fewer than five original associations were excluded. Additionally, the set of disproportionately over-reported drug-event combinations in three specific countries at three different time points were clinically assessed for labelledness. These time points corresponded to database sizes of less than 10,000, 5000 and 1000 reports, respectively. All disproportionality analysis was based on the Information Component (IC), implemented as IC > 0.
Spuriousness rates were below 0.15 for all 110 included countries regardless of subset size, with only seven countries (6%) exceeding the empirical threshold of 0.10 observed for large subsets. All 21 excluded countries had < 500 reports. For random subsets containing 3000-5000 or more reports, the higher end of observed spuriousness rates was close to 0.10. In the clinical assessment, the proportion of labelled or otherwise known drug-event combinations was very high (87-100%) across all countries and time points studied.
To mitigate the risk of highlighting spurious associations with disproportionality analysis, a minimum size of 500 reports is recommended for national databases. For databases or subsets that are not country-specific, our recommendation is 5000 reports. This study does not consider sensitivity, which is expected to be poor in smaller databases.
通过收集和评估个别病例报告来发现安全性信号仍然是药物警戒活动的核心内容。尽管比例失衡分析在信号检测中得到了广泛应用，但关于检测稳健性所需的数据库或数据库子集的最小规模的建议仍有所欠缺。
本研究旨在探讨数据库规模与比例失衡分析稳健性之间的关系，以限制虚假关联。
从全球数据库 VigiBase 中创建了三种类型的子集：随机子集（每个固定大小的子集 500 个重复，大小范围为 250 至 100000 个报告）、特定国家子集（原始 VigiBase 提取中可用的所有 131 个国家）和基于解剖治疗化学分类的子集。对于每个子集，计算了一个虚假率，该虚假率是在子集的随机版本中通过比例失衡分析突出显示的药物-事件组合数量与原始子集中相应数量的比值。在随机数据中，所有药物与不良事件之间真实的报告关联都被打破。排除了少于五个原始关联的子集。此外，还对三个特定国家在三个不同时间点的比例失衡过度报告的药物-事件组合进行了临床评估，这些时间点分别对应于数据库规模小于 10000、5000 和 1000 个报告。所有比例失衡分析均基于信息成分（IC），采用 IC > 0 实施。
无论子集大小如何，所有 110 个纳入的国家的虚假率均低于 0.15，只有 7 个国家（6%）超过了在大型子集中观察到的 0.10 的经验阈值。所有被排除的国家的报告数均<500。对于包含 3000-5000 个或更多报告的随机子集，观察到的虚假率上限接近 0.10。在临床评估中，在所研究的所有国家和时间点，标记或已知的药物-事件组合的比例都非常高（87-100%）。
为了降低比例失衡分析中突出虚假关联的风险，建议国家数据库的最小报告数为 500 个。对于非特定国家的数据库或子集，我们的建议是 5000 个报告。本研究未考虑敏感性，预计在较小的数据库中敏感性较差。
