An exploratory machine learning workflow for the analysis of adverse events from clinical trials
A new pharmaceutical drug needs to be shown to be safe and effective before it can be used to treat patients. Adverse events (AEs) are potential side-effects that are recorded during clinical trials, in which a new drug is tested in humans, and may or may not be related to the drug under study. The large diversity of AEs and the often low incidence of each AE reported during clinical trials makes traditional statistical testing challenging due to problems with multiple testing and insufficient power. Therefore, analysis of AEs from clinical trials currently relies mainly on manual review of descriptive statistics. The aim of this thesis was to develop an exploratory machine learning approach for the objective analysis of AEs in two steps, where possibly drug-related AEs are identified in the first step and patient subgroups potentially having an increased risk of experiencing a particular drug sideeffect are identified in the second step. Using clinical trial data from a drug with a well-characterized safety profile, the machine learning methodology demonstrated high sensitivity in identifying drug-related AEs and correctly classified several AEs as being linked to the underlying disease. Furthermore, in the second step of the analysis, the model suggested factors that could be associated with an increased risk of experiencing a particular side-effect, however a number of these factors appeared to be general risk factors for developing the AE independent of treatment. As the method only identifies associations, the results should be considered hypothesisgenerating. The exploratory machine learning workflow developed in this thesis could serve as a complementary tool which could help guide subsequent manual analysis of AEs, but requires further validation before being put into practice.