Statistical Signal Detection as a Routine Pharmacovigilance Practice: Effects of Periodicity and Resignalling...

The goal of signal detection in pharmacovigilance (PV) is to detect unknown causal associations between medicines and unexpected events. Statistical methods serve to detect signals and supplement traditional PV methods. Statistical signal detection (SSD) requires decisions about various settings that influence the quality and efficiency of SSD, as shown in several studies. To our knowledge, the effects of SSD periodicity and resignalling criteria on the quality and workload of routine SSD have not been published before.

To analyse the effects of different periodicities and resignalling criteria on signal detection quality and signal validation workload, and to test the impact of changing the signal threshold for number of cases.

We calculated signals of disproportionate reporting (SDRs) using thresholds of number of cases (N) ≥3, proportional reporting ratio ≥2 and Chi(2) ≥ 4. We retrospectively simulated recurrent SDR calculation and validation with varying periodicity (quarterly vs. monthly), resignalling criteria, and N ≥ 3 vs. N ≥ 5.

Changing the periodicity from quarterly to monthly increased the workload by 46.6 % (0 % signal loss). More restrictive resignalling criteria reduced the workload between 36.3 % (0 % signal loss) and 74.1 % (50 % signal loss). For N ≥ 3, the most efficient monthly SSD resignalling criterion reduced the workload by 36.3 % and detected all true signals earlier than quarterly SSD. N ≥ 5 reduced the workload between 13.8 and 21.4 % (0 % signal loss).

In real-life PV practice, signal detection and validation are recurrent periodic activities. Some true signals are only discovered upon resignalling. Our results demonstrate resignalling criteria with high signal detection quality and high efficiency. We found potential earlier detection of true signals using monthly SSD. Additional studies about resignalling should be performed to complement our findings.