Looking to list your PhD opportunities? Log in here.
This project is no longer listed on FindAPhD.com and may not be available.
Click here to search FindAPhD.com for PhD studentship opportunitiesAbout the Project
Overview
This project will appeal to individuals with expertise and interest in statistics who are enthusiastic to apply their skills to improving health of patients. The student will develop and evaluate new Bayesian approaches for re-estimating the sample size needed for a clinical trial as it progresses.
Often parameters that affect the sample size needed for a trial are not known precisely. Sample size reestimation (SSR) approaches introduce an interim analysis to assess assumptions. We are keen to investigate methods for Bayesian SSR that can allow incorporating other sources of data, in conjunction with the trial data, to improve the re-estimation. We are interested in whether such approaches can be useful for improving the chances of a successful drug development programme.
The student would be based within the Biostatistics Research Group, and would have the opportunity to work at GSK for three months within the Innovation Hub.
Number Of Awards
1
Start Date
September 2023
Award Duration
4 years
Sponsor
EPSRC ICASE and GSK
Supervisors
Professor James Wason (Newcastle), Dr Adrian Mander (GSK)
Eligibility Criteria
You should have at least a 2:1 honours degree or international equivalent strong mathematical or statistical background. Enthusiasm for research, the ability to think and work independently, excellent analytical skills and strong verbal and written communication skills are also essential requirements.
Home and international applicants (inc. EU) are welcome to apply and if successful will receive a full studentship. Applicants whose first language is not English require an IELTS score of 6.5 overall with a minimum of 5.5 in all sub-skills.
How To Apply
To apply for a studentship, you must register and apply through the University’s Apply to Newcastle Portal
Once registered select ‘Create a Postgraduate Application’.
Use ‘Course Search’ to identify your programme of study:
- you can search for the ‘Course Title’ using the programme code: 8370F
- select ‘PhD Population Health Sciences (FT) - Population and Health Sciences' as the programme of study
You will then need to provide the following information in the ‘Further Questions’ section:
- a ‘Personal Statement’ - upload a document or write a statement directly in to the application form
- the studentship code PH034 in the ‘Studentship/Partnership Reference’ field
- when prompted for how you are providing your research proposal - select ‘Write Proposal’. You should then type in the title of the research project from this advert - you do not need to upload a research proposal.
In addition, before you submit your application you will need to upload the following supporting documentation:
- covering letter and CV. The covering letter must state the title of the studentship, quote reference code PH034 and state how your interests and experience relate to the project
- degree transcripts and certificates and, if English is not your first language, a copy of your English language qualification if already completed.
Contact Details
For further information, please contact [Email Address Removed]
Funding Notes

Search suggestions
Based on your current searches we recommend the following search filters.
Check out our other PhDs in Newcastle, United Kingdom
Check out our other PhDs in United Kingdom
Start a New search with our database of over 4,000 PhDs

PhD suggestions
Based on your current search criteria we thought you might be interested in these.
PhD Studentship: Control methods for reliable sensing information in interconnected energy systems
University College London
Model-driven data fusion methods for high-dimensional heterogeneous movement and physiological datasets. Exeter Biomedical Research Centre - NIHR funded PhD Studentship for 2023/24 Entry,
University of Exeter
EPSRC/SLB Systems INDUSTRIAL CASE PhD Studentship - Co-op-Solve-M: A Co-operative Algorithm Framework for Solving Large-Scale Heterogeneous Problems with Multiple Objectives
The University of Manchester