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New analytical and simulation tools in clinical oncology

Project Description

New analytical and simulation tools in clinical oncology

PhD studentship in Mathematical Sciences

School of Mathematical Sciences, University of Nottingham
and BAST Inc. Limited, Loughborough

***Enhanced stipend of £16,000 per annum plus additional support for computing equipment and travel***

Supervised by:
• Dr Gilles Stupfler and Dr Chris Brignell (Nottingham)
• Dr Blesson Chacko (BAST Inc. Ltd.)
Background: Cancer drug developers seek to show that satisfactory drug exposure increases the rate of beneficial patient responses, but without accounting for the risk of adverse effects. This paradigm is the so-called cause-specific (CS) approach to hazard modelling, in which even a marginal improvement in average patient outcomes is seen as a positive result, even if treatment induces strong adverse reactions (typically showing as dropouts in clinical trials). It relies on the Cox proportional hazards model, which can be rather restrictive.
By contrast, sub-distribution (SD) hazard modelling clearly sets apart dropout and right-censoring. As such, it classifies dropout as a competing risk. In this paradigm, a treatment will only show positive results when the effect of treatment on the response (e.g. lifetime) is substantially stronger that the effect on the dropout rate. This is what payers, rather than drug developers, are interested in.
SD hazard models are, however, more difficult to develop. An additional difficulty in oncology trials is that the median age of patients is typically high (around 70 years). Death from one of several causes during trial is therefore a distinct possibility; death is a special event in survival analysis, since a patient who leaves the study due to their death will not carry any risk forward, contrary to censored or dropping-out patients.
Aims: This PhD project will be set in the context of SD hazard modelling. The focus will be the development of fully parametric SD hazard models that:
1. account for competing events (dropout, death, …),
2. can handle covariate information such as body weight so as to produce precise analyses tailored to individual patients,
3. and are more flexible than the standard proportional hazards model.
The rationale behind the development of such models is the gradual recognition among the industry that a patient’s individual risk of wasting limited life expectancy during treatment should easily be quantified so that drug exposure can be optimised. The project will in particular involve designing a parametric method to compute the so-called cumulative incidence functions (CIFs), which enables the analyst to evaluate the effect of a covariate on the response to treatment; the use of a parametric method will allow for the development of prediction techniques with a view on improving the transition from Phase II to Phase III in oncology trials. Power and sensitivity calculations will also be carried out.
This project will be led jointly by the School of Mathematical Sciences and the private company BAST Inc. Ltd. through their scientific director Dr Joachim Grevel. The student will be primarily based at the University of Nottingham during the first year of the studentship, and then at BAST’s premises in the second, third and fourth years. Weekly supervisory meetings will be organised with BAST and the academic supervisory team for the whole duration of the studentship.

Funding Notes

Summary: The project is open to UK students as well as non-UK EU students under certain conditions (enquire at the email addresses below). Tuition fees will be paid, and we will provide an enhanced stipend of £16,000 per annum. There will also be support available for you to claim for travel and computing equipment costs. The scholarship length will be 4 years.


Eligibility/Entry Requirements: We require an enthusiastic graduate with a 1st class degree in Mathematics, preferably of the MMath/MSc level, or an equivalent overseas qualification.
This project will hinge on the analysis of censored data, as well as regression techniques, and as such, familiarity with one or both of these topics is highly desirable. While experience with more than one of the aforementioned statistical fields would be beneficial, the willingness to learn and engage with all of them is an absolute necessity.
The project also requires the student to have experience with a scientific computing software package or programming language such as MATLAB, R, C++, and/or Python.
Apply: The studentship is to start as soon as possible or at the latest by 1 October 2019. To apply please visit the University of Nottingham application page:
For any enquiries please email: [email protected] and/or [email protected]
This studentship is open until filled. Early application is strongly encouraged.

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