Lionel Van Holle

Using time-to-onset for detecting safety signals in spontaneous reports of adverse events following immunization: a proof of concept study

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).

Assessing the extent and impact of the masking effect of disproportionality analyses on two spontaneous reporting systems databases

Masking is a statistical issue by which signals are hidden by the presence of other medicines in the database. In the absence algorithm, the impact of the masking effect has not been fully investigated.

Signal detection on spontaneous reports of adverse events following immunisation: a comparison of the performance of a disproportionality-based algorithm and a time-to-onset-based algorithm.

Disproportionality methods measure how unexpected the observed number of adverse events is. Time‐to‐onset (TTO) methods measure how unexpected the TTO distribution of a vaccine‐event pair is compared with what is expected from other vaccines and events. Our purpose is to compare the performance associated with each method.

A conceptual approach to the masking effect of measures of disproportionality

Masking is a statistical issue by which true signals of disproportionate reporting are hidden by the presence of other products in the database. Masking is currently not perfectly understood. There is no algorithm to identify the potential masking drugs to remove them for subsequent analyses of disproportionality.

Post-marketing monitoring of intussusception after rotavirus vaccination in Japan.

RotarixTM was launched in November 2011 in Japan to prevent rotavirus gastroenteritis. Some studies suggest that RotarixTM may have a temporal association with a risk of intussusception (IS). We assessed a possible association between IS and RotarixTM vaccination in Japan.

Optimization of a quantitative signal detection algorithm for spontaneous reports of adverse events post immunization

To optimize the efficiency of signal detection by maximizing the proportion of true positive (TP) signals among signals detected by a disproportionality algorithm.

The upper bound to the Relative Reporting Ratio-a measure of the impact of the violation of hidden assumptions underlying some disproportionality methods used in signal detection.

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.

Pharmacoepidemiological considerations in observed-to-expected analyses for vaccines†

Observed‐to‐expected (OE) analyses, together with data mining algorithms1, 2, 3, 4, 5, 6, 7 and pharmacoepidemiological studies,8 are part of the quantitative pharmacovigilance toolkit for vaccines. While data mining algorithms generate hypotheses about potential safety concerns and pharmacoepidemiological studies test specific hypotheses or measure associations, OE analyses stand in between. The role of OE analyses is to refine previously detected signals when there is not enough information to determine whether further action is necessary. In this paper, the focus is on the OE analyses of spontaneous reports, where the observed number of cases is obtained from a spontaneous reporting system and compared with the expected number of cases calculated based on background incidence rates from independent sources, such as epidemiological studies or national statistics. Note that disproportionality data mining algorithms estimate an “OE ratio” generated based on expected and observed numbers of cases from a single spontaneous reporting system. The key requirements and statistical methods recommended for OE analyses are described in European guidelines.9, 10 Here, we discuss in more detail how to perform the analysis and deal with uncertainties. Although described here in the context of vaccines, the methodology and recommendations are in principle also applicable for other medicinal products, but additional complexities would then have to be considered. We will not discuss the use of OE analyses for sequential monitoring, which has been described elsewhere.11, 12

Signal detection based on time-to-onset: extending a new method from spontaneous reports to observational studies

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.