An algorithm to detect unexpected increases in frequency of reports of adverse events in EudraVigilance.

ABSTRACT:

Purpose

The European Medicines Agency developed an algorithm to detect unexpected increases in frequency of reports, to enhance the ability to detect adverse events that manifest as increases in frequency, in particular quality defects, medication errors, and cases of abuse or misuse.

Methods

An algorithm based on a negative binomial time-series regression model run on 6 sequential observations prior to the monitored period was developed to forecast monthly counts of reports. A heuristic model to capture increases in counts when the previous 4 observations were null supplemented the regression. Count data were determined at drug-event combination. Sensitivity analyses were run to determine the effect of different methods of pooling or stratifying count data. Positive retrosp

SUBMITTER: Pinheiro LC 

PROVIDER: S-EPMC5765515 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature