University of Nottingham
Supervisors: Professor Joe West, Clinical Professor Peter Jepsen, Associate Professor Aidan O’Keeffe, Clinical Associate Professor Colin Crooks
Liver disease is a common cause of illness and death that is increasing in western countries such as the UK and Denmark, particularly for people under 65 years old. However, the population of liver disease patients consists of people who differ with respect to both their cause of disease, and additional factors such as co-existing conditions and their general medical history. Consequently, disease progression varies substantially: some patients may die early from their liver disease, others might be more at risk of death from causes unrelated to liver disease, whereas for other patients their liver disease does not impact on overall survival.
Modelling links between patient data on past and current medical history (e.g. blood test measurements collected over time) and liver disease progression helps clinicians to identify variables associated with different disease patterns and, ultimately, will allow predictions to be made for key patient outcomes such as survival/mortality.
This studentship combines novel methods in dynamic prediction, longitudinal data analysis, survival and competing risk models, and latent class modelling within a probabilistic framework. The goal is to develop methods to significantly change how we care for patients with liver disease by identifying whether patients would benefit from specialist care, which specialty, and when or when not to intervene within the reality of a resource-stretched healthcare system.
This studentship is based in the Nottingham NIHR Biomedical Research Centre and includes access to large longitudinal population-based cohorts with liver disease, including test results, patient-level variables, and outcomes during follow-up. Collaboration between the University of Nottingham (UK) and Aarhus University (Denmark) places the student in world-leading centres for routine health data analysis, with extensive experience in implementing novel clinical epidemiological methods to study disease occurrence, aetiology, outcomes, and real-world impact. The student will be supported by a supervisory team including Professors in hepatology and epidemiology, and experts in computational statistics and model fitting using routine health care data.
Epidemiology – Training to design studies to draw meaningful conclusions from real-world, large-scale routine healthcare datasets.
Statistics – Developing cutting-edge expertise – longitudinal data analysis, survival modelling and latent class models.
Computational statistics – Training in programming and implementing statistical methodology.
Hepatology – Knowledge of chronic liver disease, its causes, outcomes, and treatments.
Funding
Three-year studentship covers tuition fees and a tax-free stipend, UK Students, or overseas students with own funding to cover overseas tuition fees.
To apply for this job please visit jobs.nottingham.ac.uk.