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Exploiting observational data in the design and analysis of clinical trials into effective diabetes treatment - PhD in Medical Studies (Research England DTP)

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  • Full or part time
    Prof J Bowden
    Dr B Sheilds
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

Project Description

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 and its effective treatment.

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 develop design and analysis frameworks to test updated algorithms in pragmatic trial settings, incorporating real world data to further improve trial findings and better reflect routine practice.

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, statisticians and clinicians

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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