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Fully funded PhD studentship. Probabilistic forecasting for improving clinical trials in frailty


   School of Mathematics and Statistics

  Dr Miguel Juarez, , Prof Francesco Landi, , Dr Clare Lankester  Saturday, August 27, 2022  Funded PhD Project (UK Students Only)

About the Project

Dunhill Medical Trust and Healthy Lifespan Institute Doctoral Training Programme Studentship

This three and a half (3.5) year studentship is part of the newly formed Dunhill Medical Trust and Healthy Lifespan Institute Doctoral Training Programme at The University of Sheffield. We aim to train the next generation of researchers to advance the understanding of the mechanisms of ageing, and to find new effective ways to improve the lives of older people living with the multiple age-related diseases that adversely impact quality of life as we age, cause disability and frailty, and result in significant costs to health and social care services.

Research Project

Frailty affects 25—50% of people over the age of 80, the fastest growing segment of the population. Patients with frailty have reduced resilience and often lose independence following a minor adverse event. 

New therapies are emerging to prevent frailty and boost resilience, but testing such interventions is constrained by the complexities in carrying out trials for patients with frailty.  Consequently, large samples and measuring multiple outcomes are needed to test any intervention at present, with the ensuing high costs discouraging investigators from undertaking such studies.

In this project you will collaborate closely with statisticians, clinicians and regulatory experts in designing and developing cutting edge probabilistic methodology enabling simulations of virtual patients and their integration with frailty clinical trials data. 

The approach will build a library of statistical models, tailored to the end points of the clinical trial, and informed by clinical, mathematical and other relevant sources of available information. Models will then be scored and combined in a single prediction to be used in an augmented clinical trial, thus propagating clinical and model uncertainty in a coherent way. 

This forecasting and information sharing system will undergo a verification and validation process, producing guidelines and recommendations for its use that will contribute to regulatory science.

Entry Requirements:

Candidates must have:

-       Upper second class honours degree (2.1) or above in Mathematics, Applied Mathematics, Statistics, Physics, Bioinformatics, Data Science.

-       Candidates will be expected to provide a convincing justification as to why they would like to undertake the project in their application statement, demonstrating any research knowledge and, if applicable, any experience relevant to the project.

-       Candidates must be Home students

To apply:

Complete a Postgraduate Research application form here. Please state the title of the studentship, the main supervisor and select School of Mathematics and Statistics as the department. 

We encourage applicants to make informal enquiries to Miguel Juarez ()


Funding Notes

Each studentship will be supported for 3.5 years with the student expected to submit their thesis by the end of this funding period, receiving:
- stipend and fees funded at UKRI levels
- a £5000 Research Training Support Grant per year
- £300 travel budget per year

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