The University of Bath is inviting applications for the following PhD project commencing in October 2022.
Intended Supervisory Team:
Dr Thomas Burnett, Department of Mathematical Sciences, University of Bath (lead supervisor)
Dr Robin Mitra, School of Mathematics, Cardiff University
Project Summary:
Adaptive designs for clinical trials add flexibility to the clinical development process, using pre-planned interim analyses to allow alterations to a trial in progress. Such flexibility allows for more ethical and efficient trials. These benefits have been highlighted during the Covid-19 pandemic with a large amount of uncertainty while designing trials.
In most trials, there is a delay between administration of the treatment and observing the patient’s response. Thus, clinical endpoints will not be observed for all recruited patients when interim decisions need to be made, creating a missing data problem. It is crucial to make well-informed decisions at the interim analyses to ensure the best treatments are made available to patients, while avoiding delays to the trial.
Typically, a design might use either ad hoc methods or make very strong distributional assumptions, to handle this missing information. You will explore this problem from a missing data perspective, developing principled statistical methodology to improve the quality of interim decision making in adaptive clinical trials, through the incorporation of all available information. You will also have the opportunity to develop links with the pharmaceutical industry, seeking collaboration to pave the way in developing methods that are suitable for use in practical settings.
Approximate Timeline:
Year 1 – Review relevant literature; construct a general framework for the research; identify limitations with the current approaches in this setting, both theoretically and empirically (via the simulations).
Year 2 – Develop new methodology appropriate to this setting utilising missing data techniques; rigorously evaluate the performance of the proposed methodology, comparing and contrasting this to the performance of existing methods; begin dissemination of the research to the relevant communities; prepare a paper for publication in a reputable Statistics Journal.
Year 3 – Determine mathematical properties of the proposed methodology; identify theoretical advantages the proposed methods offer over existing methods; consider possible extensions to the research findings so far; disseminate the research extensively via presentations and preparing further publications; prepare the thesis.
Training and Development Opportunities:
You will have the opportunity to attend various national training courses in Statistics. These will include courses run by the Academy for PhD Training in Statistics as well as the Statisticans in the Pharmaceutical Industry. The advanced methods in Statistics and Clinical Trials you will learn in these courses will allow you to develop key research skills appropriate for your PhD, as well as being useful for your career, in general, going forward.
You will also have various networking opportunities within both academic communities and the pharmaceutical industry. This will involve attending and presenting at workshops, conferences, and seminars. There will also be further career development opportunities through attending events such as the Young Statisticians’ Meeting and the Research Students’ Conference.
More generally you will gain experience in teaching through assisting in delivering undergraduate and postgraduate courses. You will also gain experience in academic writing through preparing publications on your research and submitting these to reputable Statistics journals.
Project keywords: Clinical trials, medical statistics, missing data.
Candidate Requirements:
Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree (or the equivalent). A master’s level qualification would also be advantageous.
Non-UK applicants must meet our English language entry requirement.
Enquiries and Applications:
Informal enquiries are welcomed and should be directed to Dr Thomas Burnett.
Formal applications should be made via the University of Bath’s online application form for a PhD in Statistics.
More information about applying for a PhD at Bath may be found on our website.
Funding Eligibility:
To be eligible for funding, you must qualify as a Home student. The eligibility criteria for Home fee status are detailed and too complex to be summarised here in full; however, as a general guide, the following applicants will normally qualify subject to meeting residency requirements: UK nationals (living in the UK or EEA/Switzerland), Irish nationals (living in the UK or EEA/Switzerland), those with Indefinite Leave to Remain and EU nationals with pre-settled or settled status in the UK under the EU Settlement Scheme). This is not intended to be an exhaustive list. Additional information may be found on our fee status guidance webpage, on the GOV.UK website and on the UKCISA website.
Exceptional Overseas students (e.g. with a UK Master’s Distinction or international equivalent and relevant research experience), who are interested in this project, should contact the lead supervisor in the first instance to discuss the possibility of applying for supplementary funding.
Equality, Diversity and Inclusion:
We value a diverse research environment and aim to be an inclusive university, where difference is celebrated and respected. We welcome and encourage applications from under-represented groups.
If you have circumstances that you feel we should be aware of that have affected your educational attainment, then please feel free to tell us about it in your application form. The best way to do this is a short paragraph at the end of your personal statement.