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.

中文翻译：

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Elanco 动物健康药物警戒数据库中信号检测算法的评估

用于检测安全信号的统计算法开始应用于动物健康药物警戒 (PV) 数据库。这些信号检测算法 (SDA) 如何在动物健康 PV 数据库中执行是本报告的主题。针对一组已知信号对统计方法和 SDA 进行了评估，以确定哪些 SDA 最适合使用 Elanco 动物健康 PV 数据库进行信号检测。为四个物种的 31 种产品创建了一组应该发出信号的不良事件参考集。对照参考集评估了基于五种不成比例统计方法的九种 SDA。性能指标是灵敏度、精确度、特异性、准确度和 F 分数。对于牛和猪产品，观察到预期 (O/E) SDA 在几何距离方面最接近 100% 灵敏度和 100% 精度。对于犬和猫产品，信息组件 (IC) SDA 在几何上最接近 100% 的灵敏度和 100% 的精度。主成分分析证实，O/E 和 IC SDA 相对于彼此和其他 SDA 而言是独一无二的。SDA 的性能取决于统计方法的选择，动物物种之间存在差异。