Methods for drug safety signal detection in longitudinal observational databases: LGPS and LEOPARD

Abstract

Purpose: 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.

Method: 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.

Results: 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.

Conclusions: 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.