Background to the project: Composite responder endpoints classify trial patients as ‘responders’ or ‘non-responders’ based on whether individual component measurements cross predefined thresholds. These endpoints are common in chronic, complex autoimmune diseases, such as systemic lupus erythematosus (SLE), where it is difficult to specify a single measure of efficacy. Composite responder events may represent positive outcomes, such as remission, or negative ones, such as occurrence of relapse or flare.
Previous research has proposed ‘augmented’ analysis methods(1) that utilise information on the continuous components of responder endpoints to substantially increase statistical power. These methods are computationally difficult to apply when the endpoint is measured at multiple timepoints. They can also not be applied for time-to-event outcomes, which would be required for analysing time to flare or duration of response. If methods could be extended to these cases, a wider variety of clinical trials in SLE and other diseases could benefit from improved power; patients would also benefit from improved evidence about best treatment.
What the studentship will encompass: This project would involve developing improved statistical and computational approaches to allow the augmented analysis method to be used in a wider variety of trials. The main focus would be on SLE, although the methodology would be relevant to a wider array of diseases.
The project would involve: 1) extending previous approaches to allow for a higher number of analysis timepoints, which would involve applying computationally efficient methods for multidimensional integration; 2) implementing a parametric time-to-event approach that allows for continuous components using a latent variable model to estimate the hazard ratio, extending previous work(2); 3) improving the robustness of the augmented approaches to assumptions such as multivariate normal distribution of continuous components.
As a CASE-award, the student would benefit from spending time working with expert statisticians at UCB, and getting access to data from real SLE trials.
The primary academic supervisor for this project is James Wason, Professor of Biostatistics in the Biostatistics Research Group and NIHR Research Professor. James has led development and application of augmented analysis approaches. The student would also be supervised by Pete Philipson, Senior Lecturer in Statistics at Newcastle University’s School of Mathematics, Statistics and Physics. Pete has research expertise in joint modelling approaches and in implementation of statistical methods in software. The industrial supervisor is Margaret Jones, Head of Centre of Excellence in Statistical innovation (CESI) at UCB.
The student would spend at least three months working at UCB, allowing the student to: form collaborations with UCB expert statisticians in CESI; gain access to data; and experience working in industry.
James’s NIHR Research Professorship has a Public Advisory Group which consists of patients with chronic inflammatory conditions; the student would have opportunities to discuss their work with this group and receive training in PPIE.
Candidates require appropriate undergraduate and/or Masters degree qualifications in a quantitative subject such as mathematics or statistics.
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:
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]