PURPOSE: To study whether reports related to known drug-event associations could hinder the detection of new signals by increasing the detection thresholds when using disporportionality analyses in spontaneous reporting (SR) databases. METHODS: The French SR database (2005-2006 data) was used to test this hypothesis for the following events: bleeding, headache, hepatitis, myalgia, myocardial infarction, stroke, and toxic epidermal necrolysis (TEN). For each of these, using the Proportional Reporting Ratio (PRR) and the Reporting Odds Ratio (ROR), the number of cases needed to trigger a signal out of 50, 100, and 200 reports for a hypothetical newly introduced drug were computed before and after removing from the database reports involving drugs known to be associated with the event. RESULTS: For bleeding and stroke, removing potentially competitive data resulted in a decrease of the number of cases needed to trigger a signal for a newly introduced drug for both PRR and ROR (e.g., from 9 to 4, and 5 to 3 cases out of 50 reports for bleeding and stroke, respectively using the PRR). They were not or only slightly modified for the other studied events. CONCLUSIONS: Removing reports related to known drug-event associations could increase the sensitivity of signal detection in SR databases. This should be considered when using SR databases for signal detection as it could result in earlier identification of new drug-event associations.
PURPOSE: To study whether reports related to known drug-event associations could hinder the detection of new signals by increasing the detection thresholds when using disporportionality analyses in spontaneous reporting (SR) databases. METHODS: The French SR database (2005-2006 data) was used to test this hypothesis for the following events: bleeding, headache, hepatitis, myalgia, myocardial infarction, stroke, and toxic epidermal necrolysis (TEN). For each of these, using the Proportional Reporting Ratio (PRR) and the Reporting Odds Ratio (ROR), the number of cases needed to trigger a signal out of 50, 100, and 200 reports for a hypothetical newly introduced drug were computed before and after removing from the database reports involving drugs known to be associated with the event. RESULTS: For bleeding and stroke, removing potentially competitive data resulted in a decrease of the number of cases needed to trigger a signal for a newly introduced drug for both PRR and ROR (e.g., from 9 to 4, and 5 to 3 cases out of 50 reports for bleeding and stroke, respectively using the PRR). They were not or only slightly modified for the other studied events. CONCLUSIONS: Removing reports related to known drug-event associations could increase the sensitivity of signal detection in SR databases. This should be considered when using SR databases for signal detection as it could result in earlier identification of new drug-event associations.
Authors: Mickael Arnaud; Francesco Salvo; Ismaïl Ahmed; Philip Robinson; Nicholas Moore; Bernard Bégaud; Pascale Tubert-Bitter; Antoine Pariente Journal: Drug Saf Date: 2016-03 Impact factor: 5.606
Authors: Francesco Salvo; Florent Leborgne; Frantz Thiessard; Nicholas Moore; Bernard Bégaud; Antoine Pariente Journal: Drug Saf Date: 2013-07 Impact factor: 5.606
Authors: Antoine Pariente; Paul Avillach; Francesco Salvo; Frantz Thiessard; Ghada Miremont-Salamé; Annie Fourrier-Reglat; Françoise Haramburu; Bernard Bégaud; Nicholas Moore Journal: Drug Saf Date: 2012-10-01 Impact factor: 5.606