Use of Real-World Evidence in Health Technology Assessment for Multimorbidity at Swansea University

Funding providers: Health Data Research (HDR) UK

Subject areas: Population Data Science

Project start date: 

• 1 January 2024 (Enrolment open from mid-December)

Project description: 

Healthcare decision-making has previously focussed on developing recommendations for single conditions. However, standardised care for each chronic condition in isolation can be inappropriate for individuals living with multiple long-term conditions known as multimorbidity, and may lead to unnecessary polypharmacy. This PhD studentship aims to develop a modelling framework to estimate the natural history of disease in individuals living with multimorbidity using population-scale, linked, electronic health records from the Secure Anonymised Information Linkage (SAIL) Databank Wales Multimorbidity e-Cohort (Lyons et al, 2021). This approach will allow estimation of the potential adverse effects (such as hospitalisations) of drug-on-drug interactions for the treatment of multiple conditions and associated genetic, environmental, or demographic risk factors. Further this PhD project will compare the efficacy of different combinations of treatments used in multimorbid populations, and assess potential health inequalities.   

This PhD is funded as part of the HDR UK Medicines in Acute and Chronic Care Driver Programme, which is a national collaboration that aims to understand and transform the use of medicines for patient benefit, and reduce medicines-associated harm. The Driver Programme has a particular focus on vulnerable populations including patients living with multiple long-term conditions and those experiencing health inequalities. The successful candidate will be one of several PhD students contributing to the wider HDR UK Driver Programmes and will have the opportunity to collaborate with the wider HDR UK Driver Programme Team as well as access additional training and associated events hosted by HDR UK. 

Eligibility

Candidates must hold an Upper Second Class (2.1) honours degree. Candidates will need an MSc in Statistics/Biostatistics or Epidemiology/Health Data Science (with a strong analytical component) plus programming and data analysis skills/experience in R and/or Python.

Experience of analysing large-scale linked electronic health record data and knowledge of Bayesian methods would be an advantage.

If you are eligible to apply for the scholarship (i.e. a student who is eligible to pay the UK rate of tuition fees) but do not hold a UK degree, you can check our comparison entry requirements. Please note that you may need to provide evidence of your English Language proficiency.

Due to funding restrictions, this scholarship is open to applicants eligible to pay tuition fees at the UK rate only, as defined by UKCISA regulations. 

Funding Details

This scholarship covers the full cost of UK tuition fees and an annual stipend of £18,622.

Additional research expenses will also be available.