Statistical Signal Detection as a Routine Pharmacovigilance Practice: Effects of Periodicity and Resignalling Criteria on Quality and Workload

Abstract

IntroductionThe 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.ObjectiveTo 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.MethodsWe calculated signals of disproportionate reporting (SDRs) using thresholds of number of cases (N) ≥3, proportional reporting ratio ≥2 and Chi2 ≥ 4. We retrospectively simulated recurrent SDR calculation and validation with varying periodicity (quarterly vs. monthly), resignalling criteria, and N ≥ 3 vs. N ≥ 5.ResultsChanging 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).ConclusionsIn 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.

Analyzing adverse drug reaction using statistical and machine learning methods

“…Spontaneous adverse event reports collected under voluntary reporting systems were the major sources of structured data (Table 2). [11,[28][29][30][31][32][33][34][35][36][37][38][39][40]42,43,[61][62][63][64][65][66][67] Some of the prominent SRS include the adverse event reporting system (maintained by the FDA) and VigiBase (maintained by the World Health Organization). [1] The post-marketing phase is needed to monitor high-priority adverse events and gain insights into actual drug safety profiles by reflecting on concrete clinical practice.…”

“…To identify a logistic regression with metropolis-hastings algorithm Xu et al [65] Regression Logistic FAERS To identify secondary medications for mitigating the adverse effects of a primary drug Pettit et al [66] Regression Logistic FAERS To figure out between posaconazole serum concentrations and toxicity Lerch et al [67] Disproportionate method Signals of disproportionate reporting SRS data To detect unknown causal associations between drugs and unexpected events ADR = adverse drug reaction. datasets.…”

Supervised Machine Learning-Based Decision Support for Signal Validation Classification

“…The proportional reporting ratio (PRR) is run each month as a disproportionality method to identify which of the PECs meet the criteria and thresholds for an SDR. SDR criteria are defined for first-time SDRs with number of cases ( N ) ≥ 3 and PRR ≥ 2 and chi-squared (with Yates correction) ≥ 4 [ 16 ] and for re-signals in addition with a frequency increase ≥ 50% compared with the frequency at the latest prior validation [ 17 ].…”