University of Hong Kong Featured PhD Programmes
Imperial College London Featured PhD Programmes
University of Reading Featured PhD Programmes

Mathematical models of complex healthcare implementation strategies

Faculty of Medicine and Health

, Thursday, December 03, 2020 Competition Funded PhD Project (Students Worldwide)

About the Project

The Medical Research Council (MRC) provides guidance on how to develop and evaluate complex interventions, highlighting four features of intervention complexity: i) the number of interacting components, ii) the need to characterise delivery, iii) the degree of tailoring, and iv) the multiple potential levels at which interventions work. Many component combinations are therefore possible, which makes it important to identify the optimal combination to take forward. This is often done theoretically but could be confirmed empirically, building on Design of Experiments (DoE) methods. Empirical optimisation allows theoretical models that specify how interventions work to be translated into statistical models, which accurately predict optimal combinations of components under a variety of scenarios. A gap exists, however, between the theoretical model and designing an experiment to optimise the complex intervention. One way of filling this gap would be to translate a logic model into a mathematical model that can be used to make predictions, allowing the logic model to be quantified, elaborated and refined based on theory and existing data prior to it being used to inform the design of an experiment. The MRC guidance highlights the value of modelling complex interventions but others recognise that it could go much further.

Implementation science can be defined as the scientific study of methods to promote the systematic uptake of research findings, and other evidence-based practices, into routine clinical practice, and, hence, to improve the quality and effectiveness of health services. The aim is typically to evaluate an implementation strategy (such as Audit & Feedback), which is typically directed at clinician behaviour and/or organisational change. Implementation labs have recently been proposed as a means of using existing “at scale” service implementation programmes (e.g. National Clinical Audits) to embed sequential experiments that would test different ways of delivering implementation strategies; methodological research to improve the quality of National Clinical Audits is currently a MRC priority. However, despite the most recent Cochrane Review including 140 randomised trials, it remains difficult to recommend one strategy (or combination of components) over another on empirical grounds.

Aims and Objectives
To translate logic models of Audit and Feedback (A&F) and existing data into mathematical models, which can elaborate and refine those logic models and subsequently inform the design of randomised trials of A&F. The specific objectives are:

1. To briefly review the relevant methodological and implementation literature, and to become familiar with the relevant theoretical/logic models; 
2. To explore and re-analyse a series of completed randomised trials of A&F and collate existing data on the effects of A&F; 
3. To translate existing theoretical/logic models and data into a mathematical model that describes how A&F leads to a range of outcomes under a variety of scenarios;
4. To consider and implement different approaches to eliciting and refining distributions for the parameters in your mathematical model based on clinical or expert opinion;
5. To use your mathematical model to inform the design of a randomised trial that maximises the Value of Information (VoI) gained.

You will receive training in medical statistics, experimental design, mathematical modelling, elicitation methods, VoI analysis and the application to national clinical audits.

You should hold a first degree equivalent to at least a UK upper second class honours degree in a relevant subject, including mathematics, and/or a MSc in statistics or a related area. This project would suit someone with an interest in medical statistics.

The minimum requirements for candidates whose first language is not English are::

• British Council IELTS - score of 6.5 overall, with no element less than 6.0
• TOEFL iBT - overall score of 92 with the listening and reading element no less than 21, writing element no less than 22 and the speaking element no less than 23.

How to apply
To apply for this project applicants should complete a Faculty Scholarship Application Form using the following link. and send this alongside a full academic CV, degree certificates and transcripts (or marks so far if still studying) to the Faculty Graduate School . If you are an International applicant please include evidence that you are able to fund the difference in academic fees between the UK/EU and international rate.

We also require 2 academic references to support your application. Please ask your referees to send these references on your behalf, directly to  by no later than Thursday 3 December 2020.

Funding Notes

As part of the National Institute for Health Research funded, Yorkshire and Humber Applied Research Collaboration (ARC), this scholarship will attract an annual tax-free stipend of £15,285 for up to 3 years, subject to satisfactory progress, and will cover the UK/EU tuition fees. If you are an international applicant please be aware that you will need to be able to self-fund the deficit in academic fees between the UK/EU and international fee rate. This is one of two projects available, but only one award is available to the most successful applicant.


(1) Craig et al, BMJ, 2008, 337:a1655. 
(2) Collins et al, Ann Behav Med 2005; 30: 65-73.
(3) Walwyn, Gilmour et al, ICTMC, 2015. 
(4) Gilmour, Walwyn, IBS-BIR, 2016. 
(5) Rohwer et al. 2016 
(6) Eldridge et al. J Health Serv Res Policy, 2005, 10(3): 133-142. 
(7) Greenwood-Lee et al. BMC Medical Research Methodology, 2016, 16:51. 
(8) Ivers et al. Cochrane Database of Systematic Reviews, 2012, 6: CD000259.

Email Now

Insert previous message below for editing? 
You haven’t included a message. Providing a specific message means universities will take your enquiry more seriously and helps them provide the information you need.
Why not add a message here

The information you submit to University of Leeds will only be used by them or their data partners to deal with your enquiry, according to their privacy notice. For more information on how we use and store your data, please read our privacy statement.

* required field

Your enquiry has been emailed successfully

Search Suggestions

Search Suggestions

Based on your current searches we recommend the following search filters.

FindAPhD. Copyright 2005-2020
All rights reserved.