L. Scotti, S. Romio, A. Ghirardi, A. Arfè, M. Casula, L. Hazell, F. Lapi, A. Catapano, M. Sturkenboom, G. Corrao
{"title":"Should methods of correction for multiple comparisons be applied in pharmacovigilance?","authors":"L. Scotti, S. Romio, A. Ghirardi, A. Arfè, M. Casula, L. Hazell, F. Lapi, A. Catapano, M. Sturkenboom, G. Corrao","doi":"10.2427/11654","DOIUrl":null,"url":null,"abstract":"\nPurpose. In pharmacovigilance, spontaneous reporting databases are devoted to the early detection of adverse event ‘signals’ of marketed drugs. A common limitation of these systems is the wide number of concurrently investigated associations, implying a high probability of generating positive signals simply by chance. However it is not clear if the application of methods aimed to adjust for the multiple testing problems are needed when at least some of the drug-outcome relationship under study are known. To this aim we applied a robust estimation method for the FDR (rFDR) particularly suitable in the pharmacovigilance context. \nMethods. We exploited the data available for the SAFEGUARD project to apply the rFDR estimation methods to detect potential false positive signals of adverse reactions attributable to the use of non-insulin blood glucose lowering drugs. Specifically, the number of signals generated from the conventional disproportionality measures and after the application of the rFDR adjustment method was compared. \nResults. Among the 311 evaluable pairs (i.e., drug-event pairs with at least one adverse event report), 106 (34%) signals were considered as significant from the conventional analysis. Among them 1 resulted in false positive signals according to rFDR method. \nConclusions. The results of this study seem to suggest that when a restricted number of drug-outcome pairs is considered and warnings about some of them are known, multiple comparisons methods for recognizing false positive signals are not so useful as suggested by theoretical considerations. \n","PeriodicalId":45811,"journal":{"name":"Epidemiology Biostatistics and Public Health","volume":"19 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Epidemiology Biostatistics and Public Health","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2427/11654","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Nursing","Score":null,"Total":0}
引用次数: 1
Abstract
Purpose. In pharmacovigilance, spontaneous reporting databases are devoted to the early detection of adverse event ‘signals’ of marketed drugs. A common limitation of these systems is the wide number of concurrently investigated associations, implying a high probability of generating positive signals simply by chance. However it is not clear if the application of methods aimed to adjust for the multiple testing problems are needed when at least some of the drug-outcome relationship under study are known. To this aim we applied a robust estimation method for the FDR (rFDR) particularly suitable in the pharmacovigilance context.
Methods. We exploited the data available for the SAFEGUARD project to apply the rFDR estimation methods to detect potential false positive signals of adverse reactions attributable to the use of non-insulin blood glucose lowering drugs. Specifically, the number of signals generated from the conventional disproportionality measures and after the application of the rFDR adjustment method was compared.
Results. Among the 311 evaluable pairs (i.e., drug-event pairs with at least one adverse event report), 106 (34%) signals were considered as significant from the conventional analysis. Among them 1 resulted in false positive signals according to rFDR method.
Conclusions. The results of this study seem to suggest that when a restricted number of drug-outcome pairs is considered and warnings about some of them are known, multiple comparisons methods for recognizing false positive signals are not so useful as suggested by theoretical considerations.
期刊介绍:
Epidemiology, Biostatistics, and Public Health (EBPH) is a multidisciplinary journal that has two broad aims: -To support the international public health community with publications on health service research, health care management, health policy, and health economics. -To strengthen the evidences on effective preventive interventions. -To advance public health methods, including biostatistics and epidemiology. EBPH welcomes submissions on all public health issues (including topics like eHealth, big data, personalized prevention, epidemiology and risk factors of chronic and infectious diseases); on basic and applied research in epidemiology; and in biostatistics methodology. Primary studies, systematic reviews, and meta-analyses are all welcome, as are research protocols for observational and experimental studies. EBPH aims to be a cross-discipline, international forum for scientific integration and evidence-based policymaking, combining the methodological aspects of epidemiology, biostatistics, and public health research with their practical applications.