Analysis of data on spontaneous reports of adverse events associated with drugs

[摘要] Some adverse drug reactions (ADRs) are not detected before marketing approval is givenbecause clinical trials are not suited for their detection, for various reasons [5, 23]. Drugregulatory bodies therefore weigh the potential benefits of a drug against the harms andallow drugs to be marketed if felt that the potential benefits far outweigh the harms [26,48].Associated adverse events are subsequently monitored through various means includingreports submitted by health professionals and the general public in what is commonlyreferred to as spontaneous reporting system (SRS) [19, 23, 69]. The resulting databasecontains thousands of adverse event reports which must be assessed by expert panels tosee if they are bona fide adverse drug reactions, but which are not easy to manage by virtueof the volume [6].This thesis documents work aimed at developing a statistical model for assisting in theidentification of bona fide drug side-effects using data from the United States of America’sFood and Drugs Administration’s (FDA) Spontaneous Reporting System (otherwise knownas the Adverse Event Reporting System (AERS)) [28].Four hierarchical models based on the Conway-Maxwell-Poisson (CMP) distribution[43,78] were explored and one of them was identified as the most suitable for modeling thedata. It compares favourably with the Gamma Poisson Shrinker (GPS) of DuMouchel [19]but takes a dimmer view of drug and adverse event pairs with very small observed andexpected count than the GPS.Two results are presented in this thesis; the first one, from a preliminary analysis,presented in Chapter 2, shows that problems such as missing values for age and sex thatmilitate against the optimal use of SRS data, enumerated in the literature, remain. Thesecond results, presented in Chapter 5, concern the main focus of the research mentionedin the previous paragraph.

[发布机构] University:University of Glasgow;Department:School of Mathematics and Statistics

[关键词] Pharmacovigilance, Spontaneous Reporting System, Conway-Maxwell-Poisson (CMP) Distribution