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Exploiting observational data in the design and analysis of clinical trials into effective diabetes treatment

  • Full or part time
  • Application Deadline
    Monday, December 02, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Lead Supervisor
Prof Jack Bowden, College of Medicine and Health, University of Exeter

Additional Supervisors
Dr Beverley Shields, College of Medicine and Health, University of Exeter
Dr Lauren Rodgers,College of Medicine and Health, University of Exeter

Due to a major recent award, applications are invited from students wishing to further their scientific careers by undertaking a PhD in a diabetes related area of research. Up to four studentships will be fully funded from autumn 2020 with enhanced stipends funded from a new £6 million award. This award reflects Exeter as a world renowned centre of excellence for diabetes research.

Students can select from any of the advertised four projects. These projects have been carefully selected to provide students with an excellent scientific training in an important area of diabetes research, the latest laboratory and computing skills, outstanding resources, and with world leading scientists as supervisors. They cover various aspects of diabetes research, including autoimmunity in the pancreas; neuro-endocrinology to understand the relationship between the brain, mental health and the endocrine system; gene regulation in the placenta and fetal development of the pancreas; rare genetic forms of diabetes; muscle physiology; and the use of electronic medical records to understand disease causes, treatments and progression. Students will learn a wide range of state-of-the-art techniques, which could include CRISPR-Cas9 gene editing, DNA methylation, DNA sequence analysis, muscle insulin sensitivity physiology, brain electrophysiology, medical statistics, R for statistics and data visualisation and programming in python, data science including machine learning, in vivo metabolic phenotype skills and cell biology including 3D stem cell culture. Students will have access to outstanding resources, including cohorts of >5000 patients with rare defects in insulin secretion, a world leading collection of samples for study of pancreas pathology, resources of electronic medical records and biobanks from millions of people and unique resources for studying human development of the pancreas and brain.

Project Summary

This studentship will develop novel methods to exploit observational study and patient record data to improve the design and analysis of clinical trials into Diabetes. The student will develop strong quantitative skills in Statistics & data science, as well as state-of-the-art knowledge of Diabetes, its causes consequences, and its effective treatment.

Project Description

In diabetes, there are many different treatment options, but little guidance regarding which drug works best for which individuals. As part of our precision medicine research, we are developing statistical models to help determine the optimal treatment for diabetes patients based on their clinical features such as age, BMI and blood biomarkers.

To develop these models, we have been using data from large GP records databases and randomised controlled trials (RCTs), but both have limitations. In RCTs, patients tend to reflect a narrow, selected subgroup and not all patients adhere to the study protocol or may drop out before study completion. This hinders the interpretation and generalisability of a trial’s findings in the outside world. In contrast, observational data such as GP records have real-world relevance, but the data are messy, and many variables must be controlled for to obtain estimates comparable to those from an RCT.

Despite these challenges, there is a growing interest in developing statistical methods to combine both data sources, in order to improve patient care. This PhD will explore three specific aims:

1) To develop causal inference approaches for combining observational and RCT data to improve treatment effect estimation in a trial.
2) To use these findings to further refine our treatment prediction algorithms.
3) To test updated algorithms in trial settings, incorporating real world data to further improve trial analysis and better reflect routine practice.

This PhD will offer the student the opportunity to work with leading statisticians in trial methodology and a world-renowned diabetes research team.

The student should have a solid grounding in Statistics and experience in handling and analysing medical data. The student will join a vibrant, diverse and world class interdisciplinary research team, including geneticists, biological scientists, mathematicians, computer scientists and clinicians, all studying aspects of diabetes and related conditions.

Funding Notes

This is a 3 year fully-funded PhD studentship. Stipends are at an enhanced rate of £17,059 (2020-21) and all Home/EU tuition fees are covered. Funds will also be available for travel and research costs.

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