Background to the project
Traditional stand-alone clinical trials for prevention typically face challenges of: poor recruitment, poor assumptions about relevant parameters (i.e. the size of a treatment effect), poor generalisability of results, and long follow-up periods. Currently, there is an increase in recruiting trial populations from observational cohorts where the trail design has access to a breadth of information. Within the broad area of mater protocols [1], adaptive platform trials [2] with adaptive enrichment [3] typically utilise single time point biomarkers or subgroups. By modelling the cohort population we may identify individuals who are more likely to progress and recruit these individuals into a trial. This will address a key challenge within prevention trials that typically require long follow-up to accrue sufficient clinical events.
What the studentship will encompass:
The project will develop novel trial within cohort designs based on two clinical applications developing prevention therapies: the on-going INNODIA (An Innovative Approach Towards Understanding and Arresting Type 1 Diabetes) consortium; and the European Prevention of Alzheimer’s Dementia (EPAD) consortium. Both applications focus on important clinical targets for prevention trials that typically involve long and costly recruitment, Novel trial with cohort designs have the potential to reduce participant burden. Firstly we will develop longitudinal models for disease progression within the INNODIA study, using latent class models to account for multiple latent progression trajectories. The progression model will be used to explore trail enrichment to maximise efficiency and statistical power, while minimising expected follow-up. There are challenges linking progression models and optimising trial operating characteristics, and this will build on existing work using latent class linear mixed-models to optimise the efficiency of treatment effect estimates [4]. Finally, the project will explore adaptive designs to fully leverage the linked longitudinal progression model. The project will have a direct collaboration with the INNODIA consortium, which aims to advance the prediction, evaluation, and prevention of the onset and progression of Type 1 diabetes (T1D). Prof Dayan (Cardiff) and Prof Mander (Cardiff) will provide access and input for INNODIA; the project will be informed by the on-going INNODIA study [5] and methodology development will be driven by INNODIA. Further, Dr Tom will provide access to the EPAD Longitudinal Cohort Study (LCS) for recruitment into trials (designed to support platform trial designs [6]).
As a trial statistical methodology project there will be no fieldwork aspect. There is potential for a research visit to Cardiff University to interact with the INNODIA study.
Primary supervision by Dr White (Cambridge) will involve regular meetings, Dr Tom (Cambridge) will co-supervise and include the student within their broader statistical research group. Prof Mander will participate in monthly team meetings, and Prof Dayan will act as an advisor for the INNODIA application (participating in team meetings).
A quantitative background, for example a statistics/mathematics undergraduate degree and/or Masters degree, is essential for this statistical methodology project. Familiarity with statistical programming is helpful, but not essential (appropriate training will be provided).
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]
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