Estimating time-to-onset of adverse drug reactions from spontaneous reporting databases
| العنوان: | Estimating time-to-onset of adverse drug reactions from spontaneous reporting databases |
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| المؤلفون: | Françoise Haramburu, Hélène Théophile, Pascale Tubert-Bitter, Jean-Yves Dauxois, Fanny Leroy |
| المساهمون: | Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Paris-Sud - Paris 11 (UP11)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Mathématiques de Toulouse UMR5219 (IMT), Université Toulouse Capitole (UT Capitole), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Service de Pharmacologie, CHU Bordeaux [Bordeaux], Pharmacoepidemiologie et évaluation de l'impact des produits de santé sur les populations, Université Bordeaux Segalen - Bordeaux 2-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM), This work was supported by the Fondation ARC (fellowship DOC20121206119 to Fanny Leroy)., BMC, Ed., Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris-Sud - Paris 11 (UP11)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Bordeaux Segalen - Bordeaux 2-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS) |
| المصدر: | BMC Medical Research Methodology BMC Medical Research Methodology, 2014, 14 (1), pp.17. ⟨10.1186/1471-2288-14-17⟩ BMC Medical Research Methodology, BioMed Central, 2014, 14 (1), pp.17. ⟨10.1186/1471-2288-14-17⟩ |
| بيانات النشر: | HAL CCSD, 2014. |
| سنة النشر: | 2014 |
| مصطلحات موضوعية: | Simulation study, Exponential distribution, Mean squared error, Databases, Factual, Drug-Related Side Effects and Adverse Reactions, Lymphoma, Epidemiology, Health Informatics, computer.software_genre, Pharmacovigilance, Bias, Statistics, Medicine, Adverse Drug Reaction Reporting Systems, Humans, Right truncation, Computer Simulation, Reporting databases, Truncation (statistics), Weibull distribution, Likelihood Functions, Risk Management, Models, Statistical, [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, Database, business.industry, Tumor Necrosis Factor-alpha, Estimator, medicine.disease, Maximum likelihood estimation, 3. Good health, Parametric estimation, Sample size determination, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business, computer, Adverse drug reaction, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, Research Article |
| الوصف: | International audience; BACKGROUND: Analyzing time-to-onset of adverse drug reactions from treatment exposure contributes to meeting pharmacovigilance objectives, i.e. identification and prevention. Post-marketing data are available from reporting systems. Times-to-onset from such databases are right-truncated because some patients who were exposed to the drug and who will eventually develop the adverse drug reaction may do it after the time of analysis and thus are not included in the data. Acknowledgment of the developments adapted to right-truncated data is not widespread and these methods have never been used in pharmacovigilance. We assess the use of appropriate methods as well as the consequences of not taking right truncation into account (naïve approach) on parametric maximum likelihood estimation of time-to-onset distribution. METHODS: Both approaches, naïve or taking right truncation into account, were compared with a simulation study. We used twelve scenarios for the exponential distribution and twenty-four for the Weibull and log-logistic distributions. These scenarios are defined by a set of parameters: the parameters of the time-to-onset distribution, the probability of this distribution falling within an observable values interval and the sample size. An application to reported lymphoma after anti TNF-¿ treatment from the French pharmacovigilance is presented. RESULTS: The simulation study shows that the bias and the mean squared error might in some instances be unacceptably large when right truncation is not considered while the truncation-based estimator shows always better and often satisfactory performances and the gap may be large. For the real dataset, the estimated expected time-to-onset leads to a minimum difference of 58 weeks between both approaches, which is not negligible. This difference is obtained for the Weibull model, under which the estimated probability of this distribution falling within an observable values interval is not far from 1. CONCLUSIONS: It is necessary to take right truncation into account for estimating time-to-onset of adverse drug reactions from spontaneous reporting databases. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1471-2288 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f4e8c05af20320e2a807494ece471480Test https://www.hal.inserm.fr/inserm-00946044/documentTest |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....f4e8c05af20320e2a807494ece471480 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14712288 |
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