title: Statistical Signal Detection as a Routine Pharmacovigilance Practice: Effects of Periodicity and Resignalling Criteria on Quality and Workload.
abstract: BACKGROUND
  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.
  
  
  OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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).
  
  
  CONCLUSIONS
  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.
authors.affiliation: Lenolution GmbH, Berlin, Germany. magnus.lerch@lenolution.com.
authors.forename: Magnus
authors.initials: M
authors.lastname: Lerch
authors.affiliation: Lenolution GmbH, Berlin, Germany.
authors.forename: Peter
authors.initials: P
authors.lastname: Nowicki
authors.affiliation: Global Pharmacovigilance, Bayer Pharma AG, Berlin, Germany.
authors.forename: Katrin
authors.initials: K
authors.lastname: Manlik
authors.affiliation: Global Pharmacovigilance, Bayer Pharma AG, Berlin, Germany.
authors.forename: Gabriela
authors.initials: G
authors.lastname: Wirsching
mesh_terms: D003198:Computer Simulation; D064420:Drug-Related Side Effects and Adverse Reactions / Q000517:prevention & control*; D006282:Health Personnel / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D060735:Pharmacovigilance; D012307:Risk Factors; D013997:Time Factors; D016526:Workload