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In clinical trials, an adverse event (AE) is defined as an untoward medical occurrence in a trial participant, ranging from symptoms that do not require medical attention through to hospital admission or death. Some AEs may be related to the trial intervention, others may be incidental. Efficient ascertainment, review and analysis of AE data is critical to the safety of participants and the reliability of results, which may be defined in terms of AEs. Regulatory requirements for rapid reporting may be demanding. E.g. in our HPS2-THRIVE trial of niacin vs. placebo in 25000 participants with cardiovascular disease over 64000 AEs were recorded over 6 years. Definition and interpretation may be challenging: a single report may correspond to multiple AEs, the same AE may be reported several times. Many  reports are altered as a consequence of clinical coding or adjudication as new information becomes available. The status of an AE report may only be finalised at the end of the trial. Streamlining the AE data processing pathway would yield substantial savings in time and cost, as well as enhancing data quality and the oversight of participant safety.

Research Experience, Research Methods and Training

The student will review existing AE processing practices, then apply analysis methodologies from computer science to produce a published model of the process that will be applicable to a wide range of clinical trial designs; this will be the main output of the project. There is currently very little published in this area; this is an opportunity to make a significant contribution to clinical trials methodology. The model will inform the design of future software systems that process AEs. The correctness and usefulness of the model will be assessed against existing trials, and against regulatory expectations for understanding how AEs are processed. The student will work on communicating relevant parts of the model to all types of users: programmers, clinicians, statisticians, and trial managers.

Field Work, Secondments, Industry Placements and Training

This project could lead to CDISC-published guidance on AE processing. CDISC have a Fellows program that the student could become involved with. CTSU’s membership of the FDA Clinical Trials Transformation Initiative will provide opportunities to explore the needs of regulators, pharma and other stakeholders and will ensure the relevance of the project outcomes for future users.


Other supervisors for this project include Dr William Stevens.

Prospective Candidate

Degree or Master’s degree in computer science or related discipline; Experience with clinical datasets; Experience in data process modelling; Good understanding of clinical trials.


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Michael Lay

Head of Project Information Science

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Martin Landray

Professor of Medicine and Epidemiology; Deputy Director, Big Data Institute

Jim Davies

Professor of Software Engineering; Director, Software Engineering Programme


Jim Davies