Pooling Different Safety Data Sources: Impact of Combining Solicited and Spontaneous Reports on Signal Detection In Pharmacovigilance

Jeremy D. Jokinen (), Rosalind J. Walley (), Michael W. Colopy (), Thomas S. Hilzinger () and Peter Verdru ()
Additional contact information
Jeremy D. Jokinen: AbbVie Inc.
Rosalind J. Walley: UCB Pharma
Michael W. Colopy: UCB Pharma
Thomas S. Hilzinger: PricewaterhouseCoopers
Peter Verdru: UCB Pharma

Drug Safety, 2019, vol. 42, issue 10, No 8, 1198 pages

Abstract: Abstract Introduction The volume of adverse events (AEs) collected, analysed, and reported has been increasing at a rapid rate for over the past 10 years, largely due to the growth of solicited programmes. The proportion of various forms of solicited case data has evolved over time, with the main relative volume increase coming from Patient Support Programmes. In this study, we sought to examine the impact of the pooling of AE report data from solicited sources with data from spontaneous sources to safety signal detection using disproportionality analysis methods. Methods Two conditions were explored in which disproportionality scores from hypothetical drugs were evaluated in a simulated safety database. The first condition held occurrence of events constant and varied solicited case volume, while the second condition varied both proportion of occurrence of events and solicited case volume. Results In the first setting, where all AE terms have the same probability to occur with any drug, increasing volumes of solicited cases while keeping occurrence of events constant leads to reduced variability in disproportionality scores, consequently reducing or eliminating identified signals of disproportionate reporting. In the second setting, varying both case volume and reporting rates can mask true safety signals and falsely identify signals where there are none. Conclusions This analysis of simulated data suggests that pooling AE data from solicited sources with spontaneous case data may impact the results of disproportionality analyses, masking true safety signals and identifying false positives. Therefore, increased volumes of safety data do not necessarily correlate with improved safety signal detection.

Date: 2019
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DOI: 10.1007/s40264-019-00843-0

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