Elanco Animal Health, Greenfield, IN, USA.
Elanco Animal Health, Basel, Switzerland.
J Vet Pharmacol Ther. 2021 Jan;44(1):107-115. doi: 10.1111/jvp.12909. Epub 2020 Sep 29.
Statistical algorithms for detecting safety signals are beginning to be applied to Animal Health Pharmacovigilance (PV) databases. How these signal detection algorithms (SDAs) perform in an animal health PV database is the subject of this report. Statistical methods and SDAs were assessed against a set of known signals in order to identify which SDAs were most appropriate for signal detection using the Elanco Animal Health PV database. A reference set of adverse events that should signal was created for 31 products across four species. Nine SDAs based on five disproportionality statistical methods were evaluated against the reference set. The performance metrics were sensitivity, precision, specificity, accuracy, and F score. For bovine and porcine products, the Observed-to-Expected (O/E) SDA was the closest in terms of geometric distance to 100% sensitivity and 100% precision. For canine and feline products, the Information Component (IC) SDA was geometrically closest to 100% sensitivity and 100% precision. Principal Component Analysis confirmed that the O/E and IC SDAs were unique performers with respect to one another and other SDAs. The performance of the SDAs was dependent on the choice of the statistical method with differences seen between animal species.
统计算法开始应用于动物健康药物警戒（PV）数据库以检测安全性信号。本报告主题为这些信号检测算法（SDA）在动物健康 PV 数据库中的性能。为了确定哪些 SDA 最适合使用礼蓝动物健康 PV 数据库进行信号检测，我们使用统计学方法和 SDA 对一组已知信号进行了评估。为四个物种的 31 种产品创建了一个应发出信号的不良事件参考集。针对参考集评估了基于五种不均一性统计方法的 9 种 SDA。性能指标包括灵敏度、精度、特异性、准确性和 F 分数。对于牛和猪产品，Observed-to-Expected（O/E）SDA 在灵敏度和精度方面最接近 100%。对于犬和猫产品，Information Component（IC）SDA 在灵敏度和精度方面最接近 100%。主成分分析证实，O/E 和 IC SDA 在彼此以及其他 SDA 方面表现独特。SDA 的性能取决于统计方法的选择，在不同动物物种之间存在差异。
