The concept of collecting Adverse Events (AEs) arose with the advent of the Thalidomide incident. Prior to this the development and marketing of drugs was not regulated in any way. It was the teterogenic effects which raised people's awareness of the damage prescription drugs could cause. This thesis will begin by describing the background to the foundation of the Committee for the Safety of Medicines (CSM) and how AEs are collected today. This thesis will investigate survival analysis, discriminant analysis and logistic regression to identify prognostic indicators. These indicators will be developed to build, assess and compare predictor models produced to see if the factors identified are similar amongst the methodologies used and if so are the background assumptions valid in this case. ROC analysis will be used to classify the prognostic indices produced by a valid cut-off point, in many medical applications the emphasis is on creating the index - the cut-off points are chosen by clinical judgement. Here ROC analysis is used to give a statistical background to the decision. In addition neural networks will be investigated and compared to the other models. Two sets of data are explored within the thesis, firstly data from a Phase III clinical trial used to assess the efficacy and safety of a new drug used to repress the advance of Alzheimer's disease where AEs are collected routinely and secondly data from a drug monitoring system used by the Department of Rheumatology at the Haywood Hospital to identify patients likely to require a change in their medication based on their blood results.