Dr Matthew Sperrin, Dr Michelle Harvie, Prof G Evans
No more applications being accepted
Competition Funded PhD Project (European/UK Students Only)
About the Project
Prognostic models are tools to predict whether a person will have an event at some point in the future based on what we know about them now [1]. For example, the event of interest may be breast cancer incidence. Building such models is a substantial area of interest for medical statisticians since these models have the potential to support both evidence based decision making and the recent drive for precision medicine.
However, these models are typically based on simple modelling approaches, such as logistic regression or Cox regression. One recent criticism of these models is they typically ignore longitudinal information available about a person’s risk factors (e.g. their weight over time) [2]. Nevertheless, statistical methods to incorporate such data are available, for example through jointly modelling longitudinal and survival data [3].
This project will consider potential benefits of applying methodology for incorporating longitudinal data in prognostic models, as well as potentially extending this methodology. The area of application will be prediction of breast cancer, and a major outcome of the PhD will be an improved prognostic model for breast cancer incidence, which has the potential to better guide surveillance and interventions for women at risk of breast cancer.
You will have access to a large family history database comprising 11,000 women, linked to breast cancer outcomes. There will also be the opportunity to validate the developed prognostic model in a larger database – PROCAS, of 57,000 women.
This project would suit students with a strong background in mathematics and statistics with an interest in applying this to medicine.
http://www.manchester.ac.uk/research/matthew.sperrin
Funding Notes
This project is to be funded under the MRC Doctoral Training Partnership. If you are interested in this project, please make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. You MUST also submit an online application form, full details on how to apply can be found on our website https://www.bmh.manchester.ac.uk/study/research/funded-programmes/mrc-dtp/
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.
References
[1] Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. Springer; 2008.
[2] Goldstein BA, Navar AM, Pencina MJ, Ioannidis JP. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inform Assoc. 2016. doi:10.1093/jamia/ocw042.
[3] Rizopoulos D. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R. CRC Press; 2012.
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