Improving cost-effectiveness of clinical trials in frailty with in silico predictions

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University of Sheffield
Application Deadline
19 Feb
Study Options
Full Time

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 those 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. This impacts quality of life and health and social care costs.

Interventions such as exercise, diet and a new class of drugs, called geroprotectors, are emerging to prevent frailty and boost resilience. However, clinical testing has been hampered by the difficulties in designing studies in patients with frailty due to the difficulties in identifying meaningful measures of outcomes, their heterogeneity and variability. This means that at present to test any intervention there is a need to recruit a high number of patients, test multiple measures to detect any meaningful effect, resulting in high costs, which discourages investigators from undertaking such studies.

In this project You will collaborate closely with statisticians, clinicians and regulatory experts working on cutting edge in silico approaches addressing these issues. Recent computational technologies aimed at augmenting clinical trials with data from computer simulations have shown potential to reduce the size and duration of clinical trials. Most of currently available technologies are based on mechanistic representations of the underlying disease. However, the multifactorial nature of physical frailty and sarcopenia and the heterogeneity of the target population hampers any attempt of successful mechanistic modelling. You will design and develop probabilistic methodology enabling simulations of virtual patients and their integration with frailty clinical trials data.

The approach will build a library of plausible models, tailored to the measured outcomes of the clinical trial and informed by clinical, mathematical and other available sources of relevant information. Models will be scored and combined in a single prediction, thus taking into account clinical and model uncertainty in a coherent way. These predictions will be treated as additional information to be formally included in the physical clinical trial, making sure their associated uncertainty is quantified and propagated. 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.

- 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.

Online interviews: 21st/22nd February - please hold this date in your diaries

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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