Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands.
Pharmacoepidemiol Drug Saf. 2011 Mar;20(3):292-9. doi: 10.1002/pds.2051. Epub 2010 Oct 13.
There is a growing interest in using longitudinal observational databases for drug safety signal detection, but most of the existing statistical methods are tailored towards spontaneous reporting. Here a sequential set of methods for detecting and filtering drug safety signals in longitudinal databases is presented.
Longitudinal GPS (LGPS) is a modification of the Gamma Poisson Shrinker (GPS) that uses person time rather than case counts for the estimation of the expected number of events. Longitudinal Evaluation of Observational Profiles of Adverse events Related to Drugs (LEOPARD) is a method that can be used to automatically discard false drug-event associations caused by protopathic bias or misclassification of the dates of the adverse events by comparing prior event prescription rates to post event prescription rates. LEOPARD can generate a single test statistic, or a visualization that can be used for more qualitative information on the relationship between drug and event. Both methods were evaluated using data simulated using the Observational medical dataset SIMulator (OSIM), including the dataset used in the Observational Medical Outcomes Partnership (OMOP) cup, a recent public competition for signal detection methods. The Mean Average Precision (MAP) was used for performance measurement.
On the OMOP cup data, LGPS achieved a MAP of 0.245, and the combination of LGPS and LEOPARD achieved a MAP of 0.260, the highest score in the competition.
The sequential use of LGPS and LEOPARD have proven to be a useful novel set of methods for drug safety signal detection on longitudinal health records.
使用纵向观察性数据库进行药物安全性信号检测的兴趣日益浓厚，但现有的大多数统计方法都是针对自发报告量身定制的。本文提出了一套用于在纵向数据库中检测和筛选药物安全性信号的顺序方法。
纵向 GPS（LGPS）是 Gamma Poisson Shrinker（GPS）的一种改进，它使用人员时间而不是病例数来估计预期事件数。纵向评价与药物相关不良事件的观察性概况（LEOPARD）是一种可以通过将先前事件的处方率与事件后处方率进行比较，自动消除由前馈偏差或不良事件日期分类错误引起的虚假药物事件关联的方法。LEOPARD 可以生成单个检验统计量，或生成可视化结果，用于更定性地了解药物与事件之间的关系。这两种方法均使用使用 Observational medical dataset SIMulator（OSIM）模拟的数据进行了评估，包括最近公开的信号检测方法竞赛——Observational Medical Outcomes Partnership（OMOP）杯使用的数据。采用平均精度（MAP）进行性能评估。
在 OMOP 杯数据中，LGPS 的 MAP 为 0.245，LGPS 和 LEOPARD 的组合的 MAP 为 0.260，在竞争中得分最高。
在纵向健康记录中，使用 LGPS 和 LEOPARD 的顺序方法已被证明是一种有用的药物安全性信号检测新方法。
