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  Clinical prediction modelling methods to improve accuracy through time and space

   Faculty of Biology, Medicine and Health

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  Dr David Jenkins  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Clinical prediction models (CPMs) are tools that predict patient outcomes based upon their demographics and clinical risk factors and are used throughout healthcare to aid decision making and for monitoring, auditing and planning. Historically, CPMs are developed and validated in a static manner such that the models (coefficients) are fixed and validation is performed at a given time point. However, healthcare is constantly evolving and there is no guarantee that a model will perform well in a given target population. Hence, models can degrade through time and space.

Improved infrastructure has increased the collection of larger, more granular, data and decreased computational burden of complex modelling approaches. Thus, providing opportunity to use data more readily for CPMs and use more sophisticated methods for implementation in healthcare. We need to ensure models remain accurate through time and space and recent improvements provide opportunity to address some of the issues and questions, such as

1. How do we continually monitor prediction models and determine when to update a CPM?

2. CPMs deployed in healthcare can change practice. Therefore, associations can change between risk factor and outcomes, which impacts the model performance in the future. Also, action based upon information provided by a model can change patient outcomes, impacting the data used for model updates, generating a feedback loop. How do we prevent these from occurring or make future adjustments?

3. CPMs can be classed as medical devices but there lacks guidance on regulation and model longevity. Should models have a ‘shelf-life’ and how would this be determined?

4. Machine learning methods are increasingly being used for clinical prediction models. How should we update such models?

5. How do we appropriately leverage information about existing models when applying them to new target populations, and how should utilisation of existing models change through time?”


Applicants must have obtained or be about to obtain a First or Upper Second class UK honours degree, or the equivalent qualifications gained outside the UK, in a relevant subject area. Applicants with a relevant master’s degree or with an interest in health data science or statistics are encouraged to apply.

Before you Apply 

Applicants must make direct contact with preferred supervisors before applying. It is your responsibility to make arrangements to meet with potential supervisors, prior to submitting a formal online application.  

How to Apply 

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website ( Informal enquiries may be made directly to the primary supervisor. On the online application form select the appropriate subject title - PhD Health Informatics.

For international students, we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences. For more information please visit

Your application form must be accompanied by a number of supporting documents by the advertised deadlines. Without all the required documents submitted at the time of application, your application will not be processed and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered. If you have any queries regarding making an application please contact our admissions team [Email Address Removed]  

Equality, Diversity and Inclusion  

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website  

Computer Science (8) Mathematics (25) Medicine (26)

Funding Notes

Applications are invited from self-funded students. This project has a Band 1 fee. Details of our different fee bands can be found on our website


Jenkins, D.A, Sperrin, M., Martin, G.P. et al. Dynamic models to predict health outcomes: current status and methodological challenges. Diagn Progn Res 2, 23 (2018).
Jenkins, D.A, Martin, G.P., Sperrin, M. et al. Continual updating and monitoring of clinical prediction models: time for dynamic prediction systems?. Diagn Progn Res 5, 1 (2021).
Reynard C, Jenkins D, Martin GP, et al. Is your clinical prediction model past its sell by date? Emergency Medicine Journal 2022;39:956-958.
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