Using big data to develop and validate clinical prediction models for survival outcomes in kidney transplant

Project description 

Kidney transplantation is the organ transplant of a kidney to a patient with end-stage kidney disease. When a donor kidney is offered to a waitlisted patient, the clinical team responsible for the care of the potential recipient must make the decision to accept or decline the offer based upon complex and variable information about the donor, the recipient and the process of transplantation. Predicting graft and patient survival following transplantation is important to support this decision-making process. While research has been conducted to predict graft failure following kidney transplantation, they did not focus on patient survival and were based on a limited set of variables. There is a clinical need to develop new statistical methods using big data to better predict graft and patient survival in transplant recipients. 

This project brings the opportunity to seek to use the linked registry data from national databases to develop and validate clinical prediction models for survival outcomes. The project will aim to integrate data from multiple sources, develop models to predict risks of graft failure and death over time, and conduct an internal and external validation of the developed prediction models. Ultimately, we will develop an accessible prediction tool using a computer application, to enable clinicians and patients to use the developed models to predict the probability of a patient developing the outcomes over time. 

The anticipated impact will enhance the optimization of patient care, through supporting decision making for both patients and clinical teams.

Eligibility 

Applicants should have (at least) a first or upper second class honours degree in an appropriate subject and preferably a relevant MSc or MRes qualification. 

The studentship is supported for four years and includes full home/EU tuition fees plus a stipend of £15,285 per annum. The studentship will only fully fund those applicants who are eligible for home/EU fees with relevant qualifications. Applicants normally required to cover overseas fees would have to cover the difference between the home/EU and the overseas tuition fee rates (approximately £12,405 per annum).

If you wish to discuss this project further informally, please contact Dr Wei (yinghui.wei@plymouth.ac.uk). However, applications must be made in accordance with the details shown below.

General information about applying for a research degree at University of Plymouth is available at: https://www.plymouth.ac.uk/student-life/your-studies/doctoral-college/applicants-and-enquirers . You can apply via the online application available on the link above and by clicking 'Apply now'.

Please mark your application FAO Doctoral College and clearly state that you are applying for a PhD studentship within the School of Engineering, Computing and Mathematics.

For more information on the admissions process contact the Doctoral College doctoralcollege@plymouth.ac.uk.

The closing date for applications is 12 noon on Friday 1 May 2020. Shortlisted candidates will be invited for interview in the last week of May. We regret that we may not be able to respond to all applications. Applicants who have not received an offer of a place by the 2 week of June should consider their application has been unsuccessful on this occasion.