Simulating adverse event spontaneous reporting systems as preferential attachment networks: application to the...

Spontaneous Reporting Systems [SRS] are critical tools in the post-licensure evaluation of medical product safety. Regulatory authorities use a variety of data mining techniques to detect potential safety signals in SRS databases. Assessing the performance of such signal detection procedures requires simulated SRS databases, but simulation strategies proposed to date each have limitations.

We sought to develop a novel SRS simulation strategy based on plausible mechanisms for the growth of databases over time.

We developed a simulation strategy based on the network principle of preferential attachment. We demonstrated how this strategy can be used to create simulations based on specific databases of interest, and provided an example of using such simulations to compare signal detection thresholds for a popular data mining algorithm.

The preferential attachment simulations were generally structurally similar to our targeted SRS database, although they had fewer nodes of very high degree. The approach was able to generate signal-free SRS simulations, as well as mimicking specific known true signals. Explorations of different reporting thresholds for the FDA Vaccine Adverse Event Reporting System suggested that using proportional reporting ratio [PRR] > 3.0 may yield better signal detection operating characteristics than the more commonly used PRR > 2.0 threshold.

The network analytic approach to SRS simulation based on the principle of preferential attachment provides an attractive framework for exploring the performance of safety signal detection algorithms. This approach is potentially more principled and versatile than existing simulation approaches.

The utility of network-based SRS simulations needs to be further explored by evaluating other types of simulated signals with a broader range of data mining approaches, and comparing network-based simulations with other simulation strategies where applicable.