Mickael Arnaud1,2, Francesco Salvo3,4,5, Ismaïl Ahmed6,7,8, Philip Robinson3,9, Nicholas Moore3,4,5,9, Bernard Bégaud3,4,5, Pascale Tubert-Bitter6,7,8, Antoine Pariente3,4,5,9. 1. Université de Bordeaux, 146 Rue Léo Saignat, BP 36, 33000, Bordeaux Cedex, France. mickael.arnaud@u-bordeaux.fr. 2. INSERM U657, Bordeaux, France. mickael.arnaud@u-bordeaux.fr. 3. Université de Bordeaux, 146 Rue Léo Saignat, BP 36, 33000, Bordeaux Cedex, France. 4. INSERM U657, Bordeaux, France. 5. CHU Bordeaux, Bordeaux, France. 6. Université de Versailles St Quentin, Villejuif, France. 7. INSERM UMR 1181, Biostatistics, Biomathematics, Pharmacoepidemiology and Infectious Diseases, Villejuif, France. 8. Institut Pasteur, Paris, France. 9. CIC Bordeaux CIC1401, Bordeaux, France.
Abstract
INTRODUCTION: The two methods for minimizing competition bias in signal of disproportionate reporting (SDR) detection--masking factor (MF) and masking ratio (MR)--have focused on the strength of disproportionality for identifying competitors and have been tested using competitors at the drug level. OBJECTIVES: The aim of this study was to develop a method that relies on identifying competitors by considering the proportion of reports of adverse events (AEs) that mention the drug class at an adequate level of drug grouping to increase sensitivity (Se) for SDR unmasking, and its comparison with MF and MR. METHODS: Reports in the French spontaneous reporting database between 2000 and 2005 were selected. Five AEs were considered: myocardial infarction, pancreatitis, aplastic anemia, convulsions, and gastrointestinal bleeding; related reports were retrieved using standardized Medical Dictionary for Regulatory Activities (MedDRA(®)) queries. Potential competitors of AEs were identified using the developed method, i.e. Competition Index (ComIn), as well as MF and MR. All three methods were tested according to Anatomical Therapeutic Chemical (ATC) classification levels 2-5. For each AE, SDR detection was performed, first in the complete database, and second after removing reports mentioning competitors; SDRs only detected after the removal were unmasked. All unmasked SDRs were validated using the Summary of Product Characteristics, and constituted the reference dataset used for computing the performance for SDR unmasking (area under the curve [AUC], Se). RESULTS: Performance of the ComIn was highest when considering competitors at ATC level 3 (AUC: 62 %; Se: 52 %); similar results were obtained with MF and MR. CONCLUSION: The ComIn could greatly minimize the competition bias in SDR detection. Further study using a larger dataset is needed.
INTRODUCTION: The two methods for minimizing competition bias in signal of disproportionate reporting (SDR) detection--masking factor (MF) and masking ratio (MR)--have focused on the strength of disproportionality for identifying competitors and have been tested using competitors at the drug level. OBJECTIVES: The aim of this study was to develop a method that relies on identifying competitors by considering the proportion of reports of adverse events (AEs) that mention the drug class at an adequate level of drug grouping to increase sensitivity (Se) for SDR unmasking, and its comparison with MF and MR. METHODS: Reports in the French spontaneous reporting database between 2000 and 2005 were selected. Five AEs were considered: myocardial infarction, pancreatitis, aplastic anemia, convulsions, and gastrointestinal bleeding; related reports were retrieved using standardized Medical Dictionary for Regulatory Activities (MedDRA(®)) queries. Potential competitors of AEs were identified using the developed method, i.e. Competition Index (ComIn), as well as MF and MR. All three methods were tested according to Anatomical Therapeutic Chemical (ATC) classification levels 2-5. For each AE, SDR detection was performed, first in the complete database, and second after removing reports mentioning competitors; SDRs only detected after the removal were unmasked. All unmasked SDRs were validated using the Summary of Product Characteristics, and constituted the reference dataset used for computing the performance for SDR unmasking (area under the curve [AUC], Se). RESULTS: Performance of the ComIn was highest when considering competitors at ATC level 3 (AUC: 62 %; Se: 52 %); similar results were obtained with MF and MR. CONCLUSION: The ComIn could greatly minimize the competition bias in SDR detection. Further study using a larger dataset is needed.
Authors: A Bate; M Lindquist; I R Edwards; S Olsson; R Orre; A Lansner; R M De Freitas Journal: Eur J Clin Pharmacol Date: 1998-06 Impact factor: 2.953
Authors: N Moore; C Kreft-Jais; F Haramburu; C Noblet; M Andrejak; M Ollagnier; B Bégaud Journal: Br J Clin Pharmacol Date: 1997-11 Impact factor: 4.335
Authors: Patrick B Ryan; Martijn J Schuemie; Emily Welebob; Jon Duke; Sarah Valentine; Abraham G Hartzema Journal: Drug Saf Date: 2013-10 Impact factor: 5.606
Authors: Antoine Pariente; Fleur Gregoire; Annie Fourrier-Reglat; Françoise Haramburu; Nicholas Moore Journal: Drug Saf Date: 2007 Impact factor: 5.606