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Click here to search FindAPhD.com for PhD studentship opportunities(MRC DTP) Prognostic models exploiting longitudinal data with application to predicting breast cancer
About the Project
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
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
[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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