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

Abstract

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.