title: [Pharmacovigilance of major parmaceutical innovation].
abstract: With the continuous improvement of international "pharmacovigilance" technology and methods,it becomes the key part of the post-marketing evaluation. This issue is based on this research background, and also means to find out the Chinese medicine safety monitor which consistents with the reality. A common problem is that those who choose a career in pharmacovigilance know how the complex data presented to us are a source of both fascination and frustration. In the 70's, for the first time data mining technology in the international pharmacovigilance turn up, we try to establish new signal detection method to make contributes to post-marketing evaluation of Chinese medicine and establishment of registration. Building the national adverse reaction reporting database is widely used in western country. Nature of the problem is that pharmacovigilance issues can come through a lot of assumptions into the statistical problems, different assumptions are for different statistical tests. Through the traditional imbalance between the proportion of fourfold table for other assumptions, few countries use in practice, this does not involve evidence, but this issue provides the introduce of the principle. Methods include the ratio of the report of the Netherlands (ROR), the proportion of reports than the UK ratio (PRR),WHO's information points (IC), the U.S. Food and Drug Administration empirical Bayes (EBS), etc. Because there is no international gold standard of the signal detection method, at first we use the simulation comparing these four methods of data mining, From the point of specificity, the sample size demand, this issue views the advantages and disadvantages of four methods and application conditions,and from a technical point of view and try to propose a new signal detection method, for example, Hierarchical Bayesian.
authors.affiliation: School of Statistics, Renmin University, Beijing 100872, China. xiang.yy06_@163.com
authors.forename: Yongyang
authors.initials: Y
authors.lastname: Xiang
authors.forename: Yanming
authors.initials: Y
authors.lastname: Xie
authors.forename: Danhui
authors.initials: D
authors.lastname: Yi
mesh_terms: D057225:Data Mining; D006801:Humans; D008516:Medicine, Chinese Traditional / Q000009:adverse effects*; D060735:Pharmacovigilance; D011358:Product Surveillance, Postmarketing