Don't miss our weekly PhD newsletter | Sign up now Don't miss our weekly PhD newsletter | Sign up now

  Biomarker selection in the joint modelling of multivariate longitudinal and time-to-event data (Advert Reference: RDF18/MPE/PHILIPSON)


   Faculty of Engineering and Environment

This project is no longer listed on FindAPhD.com and may not be available.

Click here to search FindAPhD.com for PhD studentship opportunities
  Dr P Philipson  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

The joint modelling of longitudinal and time-to-event data has become a highly-active field of research over the last twenty years. Focus was initially on models with a single continuous longitudinal biomarker and a solitary time-to-event. Early research showed that such an approach offers a demonstrable improvement over other approaches such as separate or two-stage analyses. Subsequently a framework for joint modelling emerged to handle binary and count biomarkers along with extensions to multiple event types. Software to fit such models began to emerge around ten years ago.

Large studies in disease areas such as Alzheimer’s (Alzheimer’s Disease Neuroimaging Initiative) and Parkinson’s (Parkinson’s Progression Markers Initiative) disease are now routinely collecting information on multiple longitudinal biomarkers. Such studies present a series of challenges and opportunities. On one hand, this multivariate data is likely to provide more informative predictions and to allow for better discrimination between patients. Counter to this are the computational and statistical difficulties in fitting models with several biomarkers leading to questions surrounding how to best choose from a wide selection of biomarkers or whether there are advantages in reducing the dimensionality of the longitudinal data. A further complication in moving from the univariate to the multivariate setting is the possibility of interactions amongst the biomarkers themselves, further highlighting the need for a considered approach to model selection when such rich data are available.

This research project will develop methods to efficiently and effectively handle multiple longitudinal biomarkers applied to a clinical dataset of patients with mild cognitive impairment at risk of developing Alzheimer’s disease.

Eligibility and How to Apply:
Please note eligibility requirement:
• Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score, if required.
• Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere.

For further details of how to apply, entry requirements and the application form, see:
https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. RDF18/…) will not be considered.

Deadline for applications: 28 January 2018

Start Date: 1 October 2018

Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality and is a member of the Euraxess network, which delivers information and support to professional researchers.

Funding Notes

The studentship includes a full stipend, paid for three years at RCUK rates (for 2017/18, this is £14,553 pa) and fees.

References

Joint modelling of time-to-event and multivariate longitudinal outcomes: recent developments and issues
GL Hickey, P Philipson, A Jorgensen, R Kolamunnage-Dona - BMC Medical Research Methodology, 2016

joineRML: Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes
GL Hickey, P Philipson, A Jorgensen, R Kolamunnage-Donahttps://cran.r-project.org/web/packages/joineRML/joineRML.pdf

Where will I study?