# Predicting Patient Response to New Drugs - Applications to Cost-Effectiveness Models

School of Mathematics and Physics | PHD

Applications are now CLOSED
Funding
Funded
Reference Number
MAP/2020/22
14 February 2020
Start Date
1 October 2020

### Overview

Markov models are used in many different fields of research ranging from computer science, finance, engineering, genetics, education, mathematics, and biology. A Markov model can be used to describe the process where risk of an event is continuous over time and the event can happen multiple times [1]. Markov models are useful in healthcare for modelling the progression of a disease of an individual, and have been increasingly used within cost-effectiveness models. This is partly due to recent advances in computing which permits higher processing capacities.

Cost-effectiveness models are used by many Health Technology Assessment Agencies to estimate the associated costs and outcomes of a new drug treatment compared to the standard
of care. The cost-effectiveness models usually contain a Markov component based on randomized control trial (RCT) clinical evidence and used to describe the progression of a disease
of an individual over time depending on whether or not they receive the new drug or the
standard of care. A common issue with the RCTs are that the evidence is limited and the
estimation of modelling progression of a disease for patients who receive the new drug needs
to be extrapolated beyond the trial to the time horizon of the cost-effectiveness model, usually
There are many different issues in the choice of extrapolation model [2]. Newer methods are
being developed on how to deal with the range of issues involved in modelling the progression
of a disease beyond the evidence base [3], but they are all still making several assumptions on
the long term benefit of a new drug.
This PhD will focus on simulating predictions of how patients may respond to a new drug
over time, namely beyond the clinical trial period for use in a cost-effectiveness model, to
provide better methodological approaches to modelling the uncertainty of future benefit of a
new drug.
Quantitative training such as mathematics or statistics is required for this project. An interest in the area of medical statistics, pharmacoeconomics or economic modelling and experience of any of the above will be considered an advantage.
References
[1] Sonnenberg FA, Beck JR, Markov models in medical decision making: a practical guide, Medical decision
making, Dec 1993.
[2] Latimer NG, Survival analysis for economic evaluations alongside clinical trialsextrapolation with patientlevel data: inconsistencies, limitations, and a practical guide, Medical Decision Making, Aug 2013.
[3] Jackson CH, Thompson SG, Sharples LD, Accounting for uncertainty in health economic decision
models by using model averaging, Journal of the Royal Statistical Society: Series A (Statistics in Society),
Apr 2009.

#### Funding Information

###### Project Summary
Supervisor

Mode of Study

Full-time: 3 years

#### Mathematics overview

The Mathematical Research Centre conducts world-class research in the following areas: Algebra, Analysis, Operator Algebras, Algebraic Topology, Topological Data Analysis, PDEs, Survival Analysis, Bayesian Networks, Data Analytics and Operational Research. It maintains vibrant international links with a large number of researchers around the globe and regularly hosts international conferences and research visitors.

List of researchers, their interests and upcoming PhD projects can be found at:
https://web.am.qub.ac.uk/wp/msrc/.

Mode of study / duration
Registration is on a full-time or part-time basis, under the direction of a supervisory team appointed by the University. You will be expected to submit your thesis at the end of three years of full-time registration for PhD, or two years for MPhil (or part-time equivalent).

##### Mathematics Highlights
• The School has many industry links, some of which are with Seagate Technology R&D, Andor Technology and AVX Ltd. Many of our graduates take up positions with these companies in posts such as Statistical Analysis Programmer, Trainee Accountant, Financial Engineer and Business Analyst.
##### Career Development
• Graduates from the School take up employment through a number of companies such as Allstate, AquaQ Analytics, Citigroup, Deloitte, PwC, Randox, Seagate and UCAS.
##### World Class Facilities
• Since 2014, the School has invested over £12 million in new world-class student and staff facilities. Maths and Physics students now have their own teaching centre that opened in 2016 housing experimental physics laboratories, two large computer rooms for mathematical simulations and student study plus a student interaction area.
##### Key Facts

• Students will have access to our facilities, resources and our dedicated staff. The School of Maths & Physics is one of the largest Schools in the University. Staff are involved in cutting-edge research that spans a multitude of fields.

#### Course content

##### Research Information

Research Themes
Information on the research interests and activities of academics in the Mathematical Science Research Centre can be found at https://web.am.qub.ac.uk/wp/msrc/. These interests fit into the themes: Algebra, Analysis, Data Science, Optimization and Operational Research, Partial Differential Equations, Statistics, Topology and Geometry.

##### Career Prospects

Introduction
Mathematical and statistical skills are in great demand in the economy, particularly the advanced skills developed at the PhD level.

Employment after the Course
As well as continuing in research careers, our PhD graduates have also gone on to work in finance, computing, data analysis, management and teaching. Our advisors will be happy to provide further information on the career prospects arising from your chosen research area. Further information on careers can be obtained from the School and the Faculty.

##### People teaching you

Dr David Barnes

Email: d.barnes@qub.ac.uk

Professor Ivan Todorov
Director of Research - Mathematical Sciences Research Centre

Email: i.todorov@qub.ac.uk

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##### Facilities
Students will enjoy the benefits of modern practical laboratories, extensive computer facilities and interactive spaces.

#### Entrance requirements

The minimum academic requirement for admission to a research degree programme is normally an Upper Second Class Honours degree from a UK or ROI HE provider, or an equivalent qualification acceptable to the University. Further information can be obtained by contacting the School.

##### International Students

For information on international qualification equivalents, please check the specific information for your country.

##### English Language Requirements

Evidence of an IELTS* score of 6.0, with not less than 5.5 in any component, or an equivalent qualification acceptable to the University is required. *Taken within the last 2 years.

International students wishing to apply to Queen's University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.

For more information on English Language requirements for EEA and non-EEA nationals see: www.qub.ac.uk/EnglishLanguageReqs.

If you need to improve your English language skills before you enter this degree programme, INTO Queen's University Belfast offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for admission to this degree.

• Academic English: an intensive English language and study skills course for successful university study at degree level
• Pre-sessional English: a short intensive academic English course for students starting a degree programme at Queen's University Belfast and who need to improve their English.

#### Tuition Fees

Northern Ireland (NI) £4,407 £4,407 £4,407 £16,950

Mathematics costs

There are no specific additional course costs associated with this programme.

All Students

Depending on the programme of study, there may also be other extra costs which are not covered by tuition fees, which students will need to consider when planning their studies . Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £100 per year for photocopying, memory sticks and printing charges. Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen. There are also additional charges for graduation ceremonies, and library fines. In undertaking a research project students may incur costs associated with transport and/or materials, and there will also be additional costs for printing and binding the thesis. There may also be individually tailored research project expenses and students should consult directly with the School for further information.

##### How do I fund my study?
1.PhD Opportunities

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2.Doctoral Training Centres at Queen's

Queen's has eight outstanding competitive Doctoral Training Centres, with each one providing funding for a number of PhD positions and most importantly a hub for carrying out world class research in key disciplines.

3.PhD loans

The Government offers doctoral loans of up to £26,445 for PhDs and equivalent postgraduate research programmes for English- or Welsh-resident UK and EU students, £10,000 for students in Scotland and up to £5,500 for Northern Ireland students.

4.International Scholarships

Information on Postgraduate Research scholarships for international students.

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#### How to Apply

Apply using our online Postgraduate Applications Portal go.qub.ac.uk/pgapply and follow the step-by-step instructions on how to apply.

##### Find a supervisor

If you're interested in a particular project, we suggest you contact the relevant academic before you apply, to introduce yourself and ask questions.

To find a potential supervisor aligned with your area of interest, or if you are unsure of who to contact, look through the staff profiles linked here.

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