Modelling the Time to Onset of Adverse Reactions with Parametric Survival Distributions

Abstract

It has been postulated that the time to onset of adverse drug reactions is connected to the underlying pharmacological (or toxic) mechanism of adverse drug reactions whether the reaction is time dependent or not. We have conducted a preliminary study using the parametric modelling of the time to onset of adverse reactions as an approach to signal detection on spontaneous reporting system databases. We performed a parametric modelling of the reported time to onset of adverse drug reactions for which the underlying toxic mechanism is characterized. For the purpose of our study, we have used the reported liver injuries associated with bosentan, and the infections associated with the use of the tumour necrosis factor (TNF) inhibitors, adalimumab, etanercept and infliximab, which are used in Crohn's disease and rheumatoid arthritis, reported to EudraVigilance between December 2001 and September 2006. The main results reflect the fact that the reported time to onset is a surrogate of the true time to onset of the reaction and combines three hazards (occurrence, diagnosis and reporting) that cannot be disentangled. Consequently, the modelling of the time to onset of reactions reported with TNF inhibitors showed differences that could reflect different pharmacological activities, indications, monitoring of the patients or different reporting patterns. These variations could also limit the interpretation of the parametric modelling. Some consistency that was found between the occurrences of the infections with the TNF inhibitors suggests a causal association. There are statistical issues that are important to keep in mind when interpreting the results (the impact of the data quality on the fit of the distributions and the absence of a test of hypothesis linked to the absence of a relevant comparator). The study suggests that the modelling of the reported time to onset of adverse reactions could be a useful adjunct to other signal detection methods.