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Post-authorisation safety studies (PASS) of vaccines assess or quantify the risk of adverse events following immunisation that were not identified or could not be estimated pre-licensure. The aim of this perspective paper is to describe the authors' experience in the design and conduct of twelve PASS that contributed to the evaluation of the benefit-risk of vaccines in real-world settings. We describe challenges and learnings from selected PASS of rotavirus, malaria, influenza, human papillomavirus and measles-mumps-rubella-varicella vaccines that assessed or identified potential or theoretical risks, which may lead to changes to risk management plans and/or to label updates. Study settings include the use of large healthcare databases and de novo data collection. PASS methodology is influenced by the background incidence of the outcome of interest, vaccine uptake, availability and quality of data sources, identification of the at-risk population and of suitable comparators, availability of validated case definitions, and the frequent need for case ascertainment in large databases. Challenges include the requirement for valid exposure and outcome data, identification of, and access to, adequate data sources, and mitigating limitations including bias and confounding. Assessing feasibility is becoming a key step to confirm that study objectives can be met in a timely manner. PASS provide critical information for regulators, public health agencies, vaccine manufacturers and ultimately, individuals. Collaborative approaches and synergistic efforts between vaccine manufacturers and key stakeholders, such as regulatory and public health agencies, are needed to facilitate access to data, and to drive optimal study design and implementation, with the aim of generating robust evidence., (Copyright © 2017 GSK Biologicals SA. Published by Elsevier Ltd.. All rights reserved.)

Purpose: We evaluated the use of logistic regression to model the probabilities of spontaneously reported vaccine-event pairs being adverse reactions following immunization (ARFI), using disproportionality and unexpectedness of time-to-onset (TTO) distributions as predictive variables and the presence of events in the global product information as a dependent variable., Methods: We used spontaneous reports of adverse events from eight vaccines and their labels as proxies for ARFIs. Three logistic regressions were built to predict ARFIs based on different combinations of the proportional reporting ratio (PRR; disproportionality measure) and two Kolmogorov-Smirnov (KS) tests ('between vaccines' and the 'between events') of TTO distribution: model 1, using the PRR estimate and its 95% lower confidence interval (CI) limit; model 2, using the p values of the two KS tests; and model 3, using the PRR (point estimate and lower CI limit) and both KS tests. The performance of the regressions (model fit statistics, calibration, and discrimination) was measured on 100 bootstrap samples., Results: Model 3, using two quantified causality criteria, provided the best performance for all measures. The p value of the 'between vaccines' KS test was the most significant predictive factor. Model 1 had the worst performance., Conclusions: Logistic regression allows estimation of the probability of a vaccine-event pair being an ARFI using two causality criteria at the population level assessed in spontaneous report data: the strength of association (disproportionality measure) and temporality (TTO distribution tests). Logistic regression combines and weights these causality criteria based on their respective ability to predict known safety issues.

Purpose: For disproportionality measures based on the Relative Reporting Ratio (RRR) such as the Information Component (IC) and the Empirical Bayesian Geometrical Mean (EBGM), each product and event is assumed to represent a negligible fraction of the spontaneous report database (SRD). Here, we provide the tools for allowing signal detection experts to assess the consequence of the violation of this assumption on their specific SRD., Methods: For each product-event pair (P-E), a worst-case scenario associated all the reported events-of-interest with the product of interest. The values of the RRR under this scenario were measured for different sets of stratification factors using the GlaxoSmithKline vaccines SRD. These values represent the RRR upper bound that RRR cannot exceed whatever the true strength of association., Results: Depending on the choice of stratification factors, the RRR could not exceed an upper bound of 2 for up to 2.4% of the P-Es. For Engerix™, 23.4% of all reports in the SDR, the RRR could not exceed an upper bound of 2 for up to 13.8% of pairs. For the P-E Rotarix™-Intussusception, the choice of stratification factors impacted the upper bound to RRR: from 52.5 for an unstratified RRR to 2.0 for a fully stratified RRR., Conclusions: The quantification of the upper bound can indicate whether measures such as EBGM, IC, or RRR can be used for SRD for which products or events represent a non-negligible fraction of the entire SRD. In addition, at the level of the product or P-E, it can also highlight detrimental impact of overstratification., (© 2014 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons, Ltd.)

Purpose: A proof-of-concept study has previously highlighted the added value of a method using time-to-onset (TTO) for quantitative and non-parametric signal detection on spontaneous report data. The aim of this study was to assess the added value of this new TTO signal detection method adapted to observational studies., Methods: For each adverse event collected during the conduct of an observational study of H1N1 pandemic influenza vaccine, the TTO distribution was tested against the 'follow-up distribution' from vaccination to 'lost to follow-up' by a Kolmogorov-Smirnov test. Events rejecting the null hypothesis of similar distribution were flagged as signals, and a safety physician evaluated their relevance for further medical assessment. We simulated ongoing surveillance by performing retrospective weekly signal detection based on TTO., Results: The TTO method detected 21, 15 and 4 signals within a 30-day period post-dose 1 with confidence levels set at 90%, 95% and 99%, respectively. Of these signals, 14 (67%), 10 (67%) and 2 (50%) were considered as relevant. Among the 14, six had not been identified by previous signal detection activities. When performed weekly, the Kolmogorov-Smirnov test detected 26 events as signals (alpha = 0.05). Three weeks after first participant first dose, one of the six new signals could theoretically have been detected., Conclusions: This study provided evidence that the Kolmogorov-Smirnov method can screen all TTO distributions and objectively flag the unexpected, leading to earlier detection of signals, and thus potential safety issues., (© 2014 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons, Ltd.)

Purpose: To optimize the efficiency of signal detection by maximizing the proportion of true positive (TP) signals among signals detected by a disproportionality algorithm., Methods: We compared 176 different combinations of stratification factors, sex (S), age (A), region (R) and year of report (Y), and cut-off values of a Multi-Item Gamma Poisson Schrinker (MGPS) algorithm. Spontaneous adverse event reports of eight vaccines from the GlaxoSmithKline Biologicals safety database were used. Defining events listed in the Product Information as proxy of true safety signals, we compared each algorithm performance in terms of positive predictive value (PPV). For each vaccine, each algorithm was ranked according to PPV. Median rank and overall PPV were computed across vaccines., Results: For a standard cut-off of 2, the optimal stratification factors differed by vaccine and led to a set of algorithms with a median rank of 34.5 (PPV = 0.22; 34 TP). Keeping the original SARY stratification led to optimal cut-offs that differed by vaccine and a set of algorithms with a median rank of 1.75 (PPV = 0.20; 142 TP). The optimal combination of cut-off and stratification led to different algorithms by vaccine with a median rank of 1 (PPV = 0.19; 139 TP). The best unique algorithm parameterization across vaccines was 0.8-SARY (cut-off-stratification), with a median rank of 3 (PPV = 0.20; 195 TP). The original 2-SARY was one of the worst algorithms, with a median rank of 150.75 (PPV = 0.13; 8 TP)., Conclusion: Within the scope of this study, a unique MGPS algorithm across vaccines with the original full stratification but a lowered cut-off provided major performance improvement., (Copyright © 2012 John Wiley & Sons, Ltd.)

Purpose: Disproportionality analyses (DPA) are widely used in pharmacovigilance for detecting safety signals from spontaneous reports of adverse events. In these analyses, time-to-onset (TTO; the time between vaccination and the onset of the adverse event) is rarely considered. Our objective is to assess the potential use of TTO to improve signal detection (SD)., Methods: Adverse events were defined as signals for a vaccine if the TTO distribution was significantly different from the distribution of other events for the same vaccine and from the distribution obtained with the same event for other vaccines. Distributions were compared within a time window of 30 days by using the two-sample Kolmogorov-Smirnov statistic. With the use of the product label as a proxy of true positive safety signals, TTO SD was compared with a standard DPA method (based on stratified empirical Bayesian geometric mean) for an oral live pediatric vaccine (Rotarix™) and an inactivated adult vaccine (Fluarix™)., Results: With the use of the GlaxoSmithKline spontaneous reports database for Rotarix™, 10 Medical Dictionary for Regulatory Activities preferred terms were identified as signals, and among them, five were listed in the product label. The DPA method identified only three preferred terms from the label, that is, TTO SD showed higher sensitivity and specificity. For Fluarix™, TTO SD also showed higher sensitivity but lower specificity., Conclusion: This TTO SD method is complementary, conceptually and practically, to more traditional DPA and does not share the major drawback of DPA known as the masking effect. Higher sensitivity and/or specificity can be achieved using TTO SD., (Copyright © 2012 John Wiley & Sons, Ltd.)

We present here a detailed analysis of anaphylaxis cases reported to GlaxoSmithKline safety database following vaccination with its H1N1 pandemic influenza vaccines, Pandemrix™ and Arepanrix™. Cases were assessed according to the Brighton Collaboration Case Definition (BCCD) as either confirmed diagnosis (97/395, 24.6%), insufficient information to fulfil the minimal criteria of the case definition (117/395, 29.6%) or anaphylaxis excluded (181/395, 45.8%). There was no evidence that the rate of anaphylaxis following vaccination with Pandemrix™ or Arepanrix™ is increased with respect to the rates of anaphylaxis for other vaccines. Our analysis also highlighted the challenges of reliably determining the rate of anaphylaxis as an adverse event in the postmarketing setting following mass vaccination, as anaphylaxis was excluded in 45.8% of reported cases., (Copyright © 2011 Elsevier Ltd. All rights reserved.)