- Title
- Statistical detection techniques to reduce confounding by association (co-medication) and confounding by indication in the Australian spontaneous reporting system
- Creator
- Barty, Simon
- Date
- 2005
- Type
- Text; Thesis; PhD
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/61443
- Identifier
- vital:3510
- Identifier
- https://library.federation.edu.au/record=b1285471
- Abstract
- This research is the incorporation and melding of classical and Bayesian statistical techniques into an iterative methodology aimed at reducing two major confounding issues in Adverse Drug Reactions (ADRs), confounding by association (drug therapy) and confounding by indication (medical condition) to assist in the detection of signals. [...] This study highlights the ability of the STATFILE algorithm to detect drugs that are potential signals. More importantly, it also flags those drugs that are considered to be bystander drugs or noise, consequently reducing confounding by association. [...] This work highlights the significance and viability of an automated signal detection system and its practical application for the Australian spontaneous reporting of ADRs scene and potentially the international scene.; Doctor of Philosophy
- Publisher
- University of Ballarat
- Rights
- Copyright Simon M. Barty
- Rights
- This metadata is freely available under a CCO license
- Subject
- Bayesian statistical decision theory; Statistical decision; Drugs; Australian Digital Thesis
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