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  Modelling the Impact of Diagnostic Pathways in Cancer and Cardiovascular Disease - University of Swansea (part of Health Data Research UK’s Big Data for Complex Disease Driver Programme)

   Big Data for Complex Diseases (BDCD)

  Prof Rhiannon Owen  Applications accepted all year round  Funded PhD Project (UK Students Only)

About the Project

HDR UK is funding six PhDs with leading UK universities and research organisations. This brand-new programme offers the chance to carry out a doctoral research project at the leading edge of health data science.

Each project has been selected for its scientific excellence, importance and originality and will help deliver the aims and objectives of HDR UK’s Big Data for Complex Diseases (BDCD) Driver Programme.

Graduates will be well placed for a career at the forefront of health data research.

Key details

  • Hosted by: University of Swansea
  • Lead supervisor: Dr Rhiannon Owen, Swansea University Medical School
  • Duration: Three years
  • Stipend: £19,539
  • No international fee waivers
  • Start date: October 2023 or January 2024 (flexible for the right candidate)

Project summary

Many patients who are diagnosed with cancer and/or cardiovascular disease (CVD) receive their diagnosis in Accident and Emergency (A&E). These patients tend to have more severe disease than those who receive a diagnosis from their general practice (GP).

This PhD project will develop mathematical models to predict what the benefits of receiving an earlier diagnosis (via their GP) would have been for both patients and the NHS. With the frequent co-existence of cancer and CVD, this presents an important opportunity to improve population health, and reduce potential health inequalities, by improving the diagnostic pathway of both diseases.

The project will specifically explore the epidemiology of cancer and/or CVD diagnoses, especially with respect to where, and for whom, such diagnoses are made, using population-scale data including 2.9 million individuals in the Secure Anonymised Information Linkage (SAIL) Databank Wales Multimorbidity e-Cohort.

The project will use statistical modelling and machine learning techniques to predict the impact of in-hospital diagnoses for cancer and/or CVD on patient-relevant and NHS outcomes, especially with regards to life-expectancy and quality of life. In doing so, it will provide both a benchmark against which existing diagnostic/pathway initiatives can be evaluated, as well as identifying potential inequalities and predicting the impact of new areas of system development to improve patient outcomes.

Eligibility and suitability

Applicants 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 Knowledge of Bayesian methods would be an advantage.

Click here to apply.

For further information contact  or or visit the Health Data Research UK’s website.

Mathematics (25) Medicine (26)