This project will be suitable for statisticians who are interested in applying their technical skills to improve the efficiency of clinical trials and their benefit to patients.
This project will focus on improving methods for designing and analysing adaptive multi-arm trials (see (1–3)). This would include: 1.Developing group-sequential methods that can be applied when the different stages of the trial use different types of endpoints (e.g. continuous and binary). This would potentially utilise suitable multivariate models to allow for correlation. This work is motivated by five-arm diabetic foot ulcer trial. 2.Developing improved methods for adaptive multi-arm trials that allow for multiple layers of randomisation. This is motivated by an Australian trial testing several treatments for mycobacterium abscessus. 3.Proposing methods for unbiased estimation of treatment effects following an adaptive multi-arm trial where non-binding decision rules are used for dropping ineffective treatments. This would involve proposing sensitivity analysis approaches that quantify how robust results are to the decision rule used.
As part of a supportive and dynamic environment, the student would have the opportunity to get involved in the design of real clinical trials using their methods.
Training on relevant statistical methods, statistical programming and clinical trials will be provided. 1.Wason JMS, Jaki T. Optimal design of multi-arm multi-stage trials. Stat Med. 2012;31:4269–79. 2.Wason JMS, Trippa L. A comparison of Bayesian adaptive randomization and multi-stage designs for multi-arm clinical trials. Stat Med. 2014;33(13):2206–21. 3.Wason J, Stallard N, Bowden J, Jennison C. A multi-stage drop-the-losers design for multi-arm clinical trials. Stat Methods Med Res. 2017 26 (1), 508-524
Newcastle University (https://bit.ly/2BgWVVO), Faculty of Medical Sciences (https://bit.ly/2Sp9dW0)
Name of supervisor(s):
Professor James Wason (https://bit.ly/2WHFNkY), Institute of Health and Society (https://bit.ly/2SavpDG)
Applicants must have at least a 2:1 degree in a discipline relevant to the study and (by the time of starting the PhD) a master’s degree in statistics or a subject with a substantial theoretical statistics component/equivalent research training/experience.
If English is not your first language, you must have an overall IELTS of more than 6.5 with no component less than 5.5, or equivalent.
The award is available to UK/EU applicants. Non-EU international applicants who are interested should contact the supervisor.
How to apply:
You must apply through the University’s online postgraduate application system. To do this please ‘Create a new account’ (https://bit.ly/2S81mwx).
Only mandatory fields need to be completed. However, you will need to include the following information: •insert the programme code 8300F in the programme of study section •insert the studentship code HS037 in the studentship/partnership reference field •attach a covering letter and CV no more than two pages for each. The covering letter must state the title of the studentship, quote the studentship reference code HS037 and state how your interests and experience relate to the project •attach degree transcripts and certificates and, if English is not your first language, a copy of your English language qualifications.
100% of UK/EU tuition fees paid and annual living expenses of £14,777.
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