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

Baah, Emmanuel Mensah (2014) Analysis of data on spontaneous reports of adverse events associated with drugs. PhD thesis, University of Glasgow.

Abstract

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.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Keywords: Pharmacovigilance, Spontaneous Reporting System, Conway-Maxwell-Poisson (CMP) Distribution
• Subjects: Q Science > QA Mathematics
• Colleges/Schools: College of Science and Engineering > School of Mathematics and Statistics
• Supervisor's Name: Senn, Professor Stephen J.
• Date of Award: 2014
• Depositing User: Mr. Emmanuel Mensah Baah
• Unique ID: glathesis:2014-4990
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 27 Feb 2014 08:44
• Last Modified: 27 Feb 2014 08:47
• URI: https://theses.gla.ac.uk/id/eprint/4990