Signal detection in pharmacovigilance: time for a new era?

Statistical methods can be helpful in detecting adverse drug reactions.When a drug enters the market, not all adverse drug reactions are known. An important source of information are reports of adverse drug reactions from healthcare professional and patients analyzed by Pharmacovigilance Centre Lareb. In his thesis, Joep Scholl describes several statistical methods that can be used to detect still unknown adverse drug reactions. To do this, he developed a model that is used to prioritize possible safety signals. This allows the most promising signals to be analyzed first, resulting in a more efficient way of working. Additionally, factors possibly influencing statistical analyses were investigated. This proved to be the case in low-threshold reporting, where a high number of these reports disrupt the analyses. Finally, the time from the start of a drug until the start of an adverse drug reaction was investigated as a parameter to detect new adverse drug reactions. The research described in this thesis contributes to a better and more efficient analysis of adverse drug reactions and ultimately to a safer use of medicines

The value of time-to-onset in statistical signal detection of adverse drug reactions:a comparison with disproportionality analysis in spontaneous reports from the Netherlands

PURPOSE: In pharmacovigilance, the commonly used disproportionality analysis (DPA) in statistical signal detection is known to have its limitations. The aim of this study was to investigate the value of the time to onset (TTO) of ADRs in addition to DPA.METHODS: We performed a pilot study using individual case safety reports (ICSRs) for three drugs (Cervarix®, nitrofurantoin and simvastatin) from the Lareb spontaneous reporting database. TTO distributions for drug - ADR associations were compared to other ADRs for the same drug and to other drugs for the same ADR using two-sample Anderson-Darling testing. Statistically significant associations were considered true positive (TP) signals if the association was present in the official product information of the drug. Sensitivity and specificity for the TTO method were compared with the DPA method. As a measure of disproportionality, the reporting odds ratio (ROR) was used.RESULTS: In general, sensitivity was lower, and specificity was higher for the TTO method compared to DPA. The TTO method showed similar sensitivity for all three drugs, whereas specificity was lower for Cervarix®. Eight additional TP signals were found using the TTO method compared to DPA.CONCLUSIONS: Our study shows that statistical signal detection based on the TTO alone resulted in a limited number of additional signals compared to DPA. We therefore conclude that the TTO method is of limited value for full database statistical screening in our setting. Copyright © 2016 John Wiley &amp; Sons, Ltd.</p

Time to onset in statistical signal detection revisited:A follow-up study in long-term onset adverse drug reactions

PURPOSE: In a previous study, we developed a signal detection method using the time to onset (TTO) of adverse drug reactions (ADRs). The aim of the current study was to investigate this method in a subset of ADRs with a longer TTO and to compare its performance with disproportionality analysis. METHODS: Using The Netherlands's spontaneous reporting database, TTO distributions for drug-ADR associations with a median TTO of 7 days or more were compared with other drugs with the same ADR using the two-sample Anderson-Darling (AD) test. Presence in the Summary of Product Characteristics (SPC) was used as the gold standard for identification of a true ADR. Twelve combinations with different values for the number of reports and median TTO were tested. Performance in terms of sensitivity and positive predictive value (PPV) was compared with disproportionality analysis. A sensitivity analysis was performed to compare the results with those from the previous study. RESULTS: A total of 38 017 case reports, containing 32 478 unique drug-ADR associations. Sensitivity was lower for the TTO method (range 0.08-0.34) compared with disproportionality analysis (range 0.60-0.87), whereas PPV was similar for both methods (range 0.93-1.0). The results from the sensitivity analysis were similar to the original analysis. CONCLUSIONS: Because of its low sensitivity, the developed TTO method cannot replace disproportionality analysis as a signal detection tool. It may be useful in combination with other methods

Quantification of Adverse Drug Reactions Related to Drug Switches in The Netherlands

Contains fulltext : 220534.pdf (publisher's version ) (Open Access)We performed a retrospective cohort study in the Dutch patient population to identify active substances with a relatively high number of adverse drug reactions (ADRs) potentially related to drug switching. For this, we analyzed drug switches and reported ADRs related to switching between June 1, 2009, and December 31, 2016, for a selection of 20 active substances. We also compared pharmacovigilance analyses based on the absolute, switch-corrected, and user-corrected numbers of ADRs. In total, 1,348 reported ADRs and over 23.8 million drug switches were obtained from the National Health Care Institute in The Netherlands and from Lareb, which is The Netherlands Pharmacovigilance Centre. There was no correlation between the number of ADRs and the number of switches, but, on average, we found 5.7 reported ADRs per 100,000 switches. The number was relatively high for rivastigmine, levothyroxine, methylphenidate, and salbutamol, with 74.9, 50.9, 47.6, and 26.1 ADRs per 100,000 switches, respectively. When comparing analyses using the absolute number and the switch-corrected number of ADRs, we demonstrate that different active substances would be identified as having a relatively high number of ADRs, and different time periods of increased numbers of ADRs would be observed. We also demonstrate similar results when using the user-corrected number of ADRs instead of the switch-corrected number of ADRs, allowing for a more feasible approach in pharmacovigilance practice. This study demonstrates that pharmacovigilance analyses of switch-related ADRs leads to different results when the number of reported ADRs is corrected for the actual number of drug switches