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Dynamic prediction modelling in Parkinson’s disease


Project Description

1. Background to the project:

Parkinson’s disease (PD) is a progressive, disabling, neurodegenerative disorder which is common in the elderly. Being able to predict prognosis has many benefits, including better information provision to patients; enabling personalised medicine, where treatments are tailored to an individual’s prognosis; and enhancing the design of clinical trials. These benefits can best be realised with the development of robust prognostic models, also known as clinical prediction models. These models are complex mathematical algorithms that use characteristics of a patient and/or their treatment to predict outcomes, but can be easily used by clinicians with a simple web-based interface, for example. Previous prognostic models have been developed for use in PD, but there is no clear gold standard model, and all current models have been designed for use at the time a patient is diagnosed with PD. Additional clinical features which arise during the disease course together with treatments are likely to influence prognosis. Therefore, as time goes on, the presence or absence of such features, or treatment-related variables, will allow adjustment of predictions of prognosis. Being able to estimate and update the evolving prognosis at different points in time is important for clinical decision making e.g. planning individualised interventions or planning for social care needs.

2. Proposed research and techniques:

The ideal study design to investigate prognosis in PD is the long-term follow-up of incidence cohorts. A previous systematic review identified only 6 such studies world-wide. We are leading a collaborative programme that has pooled the data from all these studies (4 in the UK, 2 in Scandinavia). We will use demographic, clinical, and genetic data from these studies to develop models using novel methods such as dynamic prediction to predict key clinical outcomes, including becoming dependent (needing help for basic activities of daily living), developing dementia, and death. Dynamic models allow us to update the chances of these outcomes at different points in time as new patient and treatment information becomes available. No previous studies have developed or validated dynamic prediction models using individual-patient data from several studies so appropriate methodology for this will have to be developed. You will join a collaborative team in the University of Aberdeen who are known in the world of PD research for their expertise in the creation of innovative prediction models and have experience in the development of dynamic prediction models in single studies in other disease settings. There be opportunities to develop collaborations with researchers in the other study centres (Universities of Cambridge, Newcastle, Stavanger, and Umea), for example around genetic predictors of prognosis in PD. We also have strong links with methodologists from Universities of Utrecht and Leiden. There will be opportunities to engage with local and national patient groups both in the development of this project, and upon development of the dynamic prognostic models, to improve the accuracy of prognostic information given to patients, and to engage with clinicians to evaluate the use of these models in clinical practice.

3. Useful previous experience for the studentship:

This project requires a numerically highly skilled student, preferably with a medical statistics qualification, who is interested in prognostic modelling and is motivated to perform high-quality research which is useful to patients with PD.

APPLICATION PROCEDURE:
This project is advertised in relation to the research areas of APPLIED HEALTH SCIENCE. Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php. You should apply for Degree of Doctor of Philosophy in Applied Health Science, to ensure that your application is passed to the correct person for processing.

NOTE CLEARLY THE NAME OF THE SUPERVISOR AND EXACT PROJECT TITLE ON THE APPLICATION FORM.

Funding Notes

This project is for self-funded applicants only – there is no funding attached.

For details of fees: View Website

Candidates should have (or expect to achieve) a minimum of a 2.1 Honours degree in a relevant subject. Applicants with a minimum of a 2.2 Honours degree may be considered provided they have a Merit/Commendation/Distinction at Masters level.

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