Decision support methods for the detection of adverse events in post-marketing data

被引:115
作者
Hauben, M. [1 ,2 ,3 ,4 ,5 ]
Bate, A. [5 ,6 ]
机构
[1] Pfizer, New York, NY USA
[2] NYU, Sch Med, New York, NY USA
[3] New York Med Coll, Valhalla, NY 10595 USA
[4] Univ Maryland, Sch Pharm, College Pk, MD 20742 USA
[5] Brunel Univ, Dept Informat Syst & Comp, London, England
[6] WHO Collaborating Ctr Int Drug Monitoring, Uppsala Monitoring Ctr, Uppsala, Sweden
关键词
DRUG-DRUG INTERACTIONS; DATA MINING APPROACH; SIGNAL GENERATION; NEURAL-NETWORK; RETROSPECTIVE EVALUATION; STATISTICAL METHODOLOGY; REACTION SURVEILLANCE; MONITORING PROGRAM; ABDOMINAL COCOON; PHARMACOVIGILANCE;
D O I
10.1016/j.drudis.2008.12.012
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Spontaneous reporting is a crucial component of post-marketing drug safety surveillance despite its significant limitations. The size and complexity of some spontaneous reporting system databases represent a challenge for drug safety professionals who traditionally have relied heavily on the scientific and clinical acumen of the prepared mind. Computer algorithms that calculate statistical measures of reporting frequency for huge numbers of drug-event combinations are increasingly used to support pharamcovigilance analysts screening large spontaneous reporting system databases. After an overview of pharmacovigilance and spontaneous reporting systems, we discuss the theory and application of contemporary computer algorithms in regular use, those under development, and the practical considerations involved in the implementation of computer algorithms within a comprehensive and holistic drug safety signal detection program.
引用
收藏
页码:343 / 357
页数:15
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