Some adverse drug reactions (ADRs) are not detected before marketing approval is given because clinical trials are not suited for their detection, for various reasons [5, 23]. Drug regulatory bodies therefore weigh the potential benefits of a drug against the harms and allow 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 including reports submitted by health professionals and the general public in what is commonly referred to as spontaneous reporting system (SRS) [19, 23, 69]. The resulting database contains thousands of adverse event reports which must be assessed by expert panels to see if they are bona fide adverse drug reactions, but which are not easy to manage by virtue of the volume [6]. This thesis documents work aimed at developing a statistical model for assisting in the identification of bona fide drug side-effects using data from the United States of America’s Food and Drugs Administration’s (FDA) Spontaneous Reporting System (otherwise known as 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 the data. 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 and expected 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 that militate against the optimal use of SRS data, enumerated in the literature, remain. The second results, presented in Chapter 5, concern the main focus of the research mentioned in the previous paragraph.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:591993 |
| Date | January 2014 |
| Creators | Baah, Emmanuel Mensah |
| Publisher | University of Glasgow |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://theses.gla.ac.uk/4990/ |
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