Using Electronic Health Care Records for Drug Safety Signal Detection: A Comparative Evaluation of Statistical Methods.

Schuemie, MJ; Coloma, PM; Straatman, H; Herings, RM; Trifirò, G; Matthews, JN; Prieto-Merino, D; Molokhia, M; Pedersen, L; Gini, R; Innocenti, F; Mazzaglia, G; Picelli, G; Scotti, L; van der Lei, J; Sturkenboom, MC; (2012) Using Electronic Health Care Records for Drug Safety Signal Detection: A Comparative Evaluation of Statistical Methods. Medical care, 50 (10). pp. 890-7. ISSN 0025-7079 DOI: 10.1097/MLR.0b013e31825f63bf

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Abstract

BACKGROUND: Drug safety monitoring relies primarily on spontaneous reporting, but electronic health care record databases offer a possible alternative for the detection of adverse drug reactions (ADRs).<br/> OBJECTIVES: To evaluate the relative performance of different statistical methods for detecting drug-adverse event associations in electronic health care record data representing potential ADRs.<br/> RESEARCH DESIGN: Data from 7 databases across 3 countries in Europe comprising over 20 million subjects were used to compute the relative risk estimates for drug-event pairs using 10 different methods, including those developed for spontaneous reporting systems, cohort methods such as the longitudinal gamma poisson shrinker, and case-based methods such as case-control. The newly developed method "longitudinal evaluation of observational profiles of adverse events related to drugs" (LEOPARD) was used to remove associations likely caused by protopathic bias. Data from the different databases were combined by pooling of data, and by meta-analysis for random effects. A reference standard of known ADRs and negative controls was created to evaluate the performance of the method.<br/> MEASURES: The area under the curve of the receiver operator characteristic curve was calculated for each method, both with and without LEOPARD filtering.<br/> RESULTS: The highest area under the curve (0.83) was achieved by the combination of either longitudinal gamma poisson shrinker or case-control with LEOPARD filtering, but the performance between methods differed little. LEOPARD increased the overall performance, but flagged several known ADRs as caused by protopathic bias.<br/> CONCLUSIONS: Combinations of methods demonstrate good performance in distinguishing known ADRs from negative controls, and we assume that these could also be used to detect new drug safety signals.<br/>

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Medical Statistics
Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology
Research Centre: Centre for Statistical Methodology
PubMed ID: 22929992
Web of Science ID: 309058700011
URI: http://researchonline.lshtm.ac.uk/id/eprint/210209

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