FindAPhD Weekly PhD Newsletter | JOIN NOW FindAPhD Weekly PhD Newsletter | JOIN NOW

Using existing data to optimise adaptive interventions in epilepsy


   Faculty of Health and Life Science

This project is no longer listed on FindAPhD.com and may not be available.

Click here to search FindAPhD.com for PhD studentship opportunities
  Prof Catrin Tudur Smith  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Background

Approximately half of patients with newly diagnosed epilepsy who start antiepileptic drug (AED) monotherapy fail on their first treatment, after which the clinician and patient will discuss the likely overall prognosis and decide on the next AED to start. Failure on the second AED may lead to further substitution of AED as monotherapy, or the addition of further AEDs as polytherapy, with the potential for a large number of alternative treatment sequences and possible adaptations. The probability of achieving seizure control reduces with every failed AED and approximately 30% of patients will have a drug-resistant form of epilepsy. Traditionally trial designs for epilepsy focus on randomising newly-diagnosed patients or drug-resistant patients as separate populations, and there is a dearth of evidence to support adaptive treatment decisions between these two points in an individual patient’s pathway.

The Sequential Multiple Assignment Randomised Trial (SMART) is an innovative clinical trial design, to provide high-quality data that can be used to inform the development of adaptive interventions across the pathway. A SMART involves multiple intervention stages where each stage corresponds to one of the critical decisions involved in the adaptive intervention, and randomises participants at each state. Although such a SMART trial would be the gold standard for causal inference, the approach would be expensive and lengthy, and utilising existing real world data by applying analytic techniques to emulate a SMART target trial would be an efficient use of currently available data and evidence, and can incorporate information about patient characteristics to personalise optimal pathways.

What the studentship will encompass:

The aim is to establish how real-world evidence can be used to optimise treatment decisions for patients with epilepsy and inform the design of a future SMART trial. Specific aims include:

1. Summarise the evidence for treatment decisions following treatment failure (review).

2. Identify risk factors for outcome following treatment failure in patients with epilepsy according (systematic review of risk factor studies and secondary analysis of existing trial datasets available at UoL).

3. Evaluate how previous SMART trials have used existing evidence in their design and analysis

4. Implement an analytic approach that emulates a target SMART trial of AED treatments using existing real world data 

5. Design a SMART trial to explore the most promising treatment sequences in epilepsy incorporating existing evidence

Catrin Tudur Smith will be primary supervisor and will meet with the student at least fortnightly. Tony Marson will provide clinical input and epilepsy expertise; Richard Emsley will provide expertise in the statistical aspects of SMART designs and causal inference; Laura Bonnett will provide expertise in prediction modelling and guidance on the datasets. Supervisors will contribute to monthly team meetings and additional meetings as required.   

No formal placements are planned as yet but the student will be offered the opportunity to visit King’s College London to meet with Richard Emsley and partake in the activities of the KCL Trials Methodology Research Group. Further opportunities will be explored. 

We have incorporated PPI and worked with patient representatives in previous epilepsy trials and methods research projects. We will seek input from our existing network of patient representatives and our links with Epilepsy Action. As a minimum we will incorporate PPI into the design of the SMART trial (e.g. to comment on the plain language summary and comment on acceptability of the design).

A first degree with a substantial statistics component is essential. A Masters degree is desirable.

HOW TO APPLY

You are applying for a PhD studentship from the MRC TMRP DTP. A list of potential projects and the application form is available online at:

http://www.methodologyhubs.mrc.ac.uk/about/tmrp-doctoral-training-partnership/

Please complete the form fully. Incomplete forms will not be considered. CVs will not be accepted for this scheme.

Please apply giving details for your first choice project. You can provide details of up to two other TMRP DTP projects you may be interested in at section B of the application form.

Before making an application, applicants should contact the project primary supervisor to find out more about the project and to discuss their interests in the research.

The deadline for applications is 4pm (GMT) 18 February 2022. Late applications will not be considered.

Completed application forms must be returned to: [Email Address Removed]

Informal enquiries may be made to [Email Address Removed]


Funding Notes

Studentships are funded by the Medical Research Council (MRC) for 3 years. Funding will cover tuition fees at the UK rate only, a Research Training and Support Grant (RTSG) and stipend (stipend to include London Weighting where appropriate). We aim to support the most outstanding applicants from outside the UK and are able to offer a limited number of bursaries that will enable full studentships to be awarded to international applicants. These full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme.
Search Suggestions
Search suggestions

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

PhD saved successfully
View saved PhDs