Modelling individual heterogeneity in response to treatment using Bayesian mixture models (Ref FHMS - FF - 23 BIO)

Clinical studies are typically performed by comparing population and group averages. As individuals frequently differ in the direction and magnitude of a treatment effect, conclusions about benefit or harm for most treatments can be deceptive and fail to distinguish the complex mixture of results indicating benefit for some, little benefit for others, and harm for a few. In clinical studies, Cox regression and frailty models are well-established methods for comparing the response to treatment between groups of individuals. However, these methods provide very limited assistance when dealing with arbitrary survival functions and heterogeneous groups of individuals.

In this PhD, you will develop a flexible and rigorous methodology to estimate treatment response that cope with censoring, heterogeneous individuals, and arbitrary survival functions. We will adopt a Bayesian model averaging framework to synthesize novel failure distributions, flexible nonlinear regression functions to accommodate arbitrary survival functions and derive a rigorous framework to estimate expected ages of onset for arbitrary survival functions. You will develop statistical software for the application of these models in the Comprehensive R Archive Network and make it publicly available. 

Training

The successful candidate will receive comprehensive research training including biostatistics, bioinformatics, and machine genetics, hands-on practice with several clinical trial and epidemiological studies, in addition to an extensive training programme in technical, personal, and professional skills at the Doctoral College of the University of Surrey. The candidate will have multiple opportunities to present his work in conferences, workshops, and seminars. 

The Research Environment

The project will be conducted at the University of Surrey, under the supervision of Dr. Alexessander Couto Alves, and Prof. Simon Skene within the Surrey Artificial Intelligence Institute, the Bioinformatics Core Facility, the Centre for Mathematical and Computational Biology, the Surrey Clinical Trials Unit, with access to other core facilities at the University of Surrey. Surrey University hosts the largest GPU facility in the UK supporting world-leading research in AI, machine learning and data sciences as part of the Joint Academic Data Science Endeavour (JADE). Besides providing a stimulating environment for cross-disciplinary collaborative research, these groups will foster multiple training opportunities for the student. The student will benefit from computing and research resources from AI Institute. At the Clinical trials unit the student will benefit from a vibrant and collegial environment with technical and biostatistics expertise and infrastructure to provide training and support.

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References 14 | 1. Couto Alves, A. et al. GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI. Science Advances 5, 2019. 2. A. Akbari, M. Awais, Z. Feng, A. Farooq, J. Kittler. “Distribution Cognisant Loss for Cross-Database Facial Age Estimation with Sensitivity Analysis”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020. 3. T. Xu, Z. Feng, X. Wu, J. Kittler. "Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking." IEEE Transactions on Image Processing, 28(11), 2019, pp: 5596-5609. 4. Z. Feng, J. Kittler, M. Awais, X. Wu. “Rectified Wing Loss for Efficient and Robust Facial Landmark Localisation with Convolutional Neural Networks”, International Journal of Computer Vision, 128, 2020, pp: 2126–2145. 5. China L, Freemantle N, Forrest E, Kallis Y, Ryder SD, Wright G, Portal AJ, Becares Salles N, Gilroy DW, O'Brien A, ATTIRE Trial Investigators. A Randomized Trial of Albumin Infusions in Hospitalized Patients with Cirrhosis. New England Journal of Medicine 384 (9), 2021 pp :808-817

Entry requirements

Open to UK and international students with the project starting in October 2023. Note that a maximum of 30% of the studentships will be offered to international students.

You will need to meet the minimum entry requirements for our PhD programme https://www.surrey.ac.uk/postgraduate/biosciences-and-medicine-phd#entry.

How to apply

Applicants are strongly encouraged to contact the relevant principal supervisor(s) to discuss the project(s) before submitting their application.

Applications should be submitted via the [https://www.surrey.ac.uk/postgraduate/biosciences-and-medicine-phd programme page (N.B. Please select the October 2023 start date when applying).

You may opt to apply for a single project or for 2 of these Faculty-funded studentship projects

When completing your application, in place of a research proposal, please provide a brief motivational document (1 page maximum) which specifies:

• the reference numbers(s) for the project or two projects you are applying for 
• the project title(s) and principal supervisor name(s) 
• if applying for two projects, please also indicate your order of preference for the projects
• an explanation of your motivations for wanting to study for a PhD 
• an explanation of your reasons for selecting the project(s) you have chosen

Additionally, to complete a full application, you MUST also email a copy of your CV and 1-page motivational document directly to the relevant project principal supervisor of each project you apply for. Due to short turnaround times for applicant shortlisting, failure to do this may mean that your application is not considered.

Please note that online interviews for shortlisted applicants are expected to take place during the week commencing 30th January.