The major aim of Pharmacovigilance is signal detection (i.e., the identification of potential drug-event association that may be novel by virtue of their nature, severity and/or frequency).
Currently, signal detection can be performed on a case-by-case basis (traditional approach, especially for Designated Medical Event – DMEs) or through automated procedures to support the clinical evaluation of spontaneous reports, “data mining approach”.
In general terms, data mining refers to the use of complex data analytics to discover patterns of associations or unexpected occurrences (“signals”) in large databases.
Data mining is refereed to as the computer-assisted procedures, starting from processing of dataset by data “cleaning” and culminating into the application of statistical techniques, often known as data mining algorithms (DMAs).
DMA Methods include:
Disproportionality methods such as Relative reporting ratio (RRR), Reporting Odds Ratio (ROR) and Proportional Reporting Ratio (PRR) are particularly appealing and therefore widely used due to the fact that they are relatively easy to understand, interpret and compute.
Bayesian methods such as Multi-item Gamma Poisson Shrinker (MGPS), information component (IC) and gamma poisson shrinker calculating EBGM and Bayesian Confidence Propagation Neural network (BCPN) are based on Bayes’ law to estimate the probability (posterior probability) that the suspected event occurs given the use of suspect drug.
