Background to the project
The nature of complex interventions, involving multifaceted components that may target multiple behaviours, often require collection of several outcome measures to track participant responses, and demonstrate the theoretical bases for how interventions may work.1-2 Assessment of multiple outcomes raises both practical and statistical challenges such as multiplicity, and the choice of appropriate outcomes has been identified as a high priority research topic both in a UK-wide and global consensus study for methodological research.3-4 The application of more sophisticated statistical analysis methods, such as multivariate modelling, rank-based analysis, and win-ratio statistic, provide the opportunity to take advantage of the linked nature of multiple outcomes widely collected and reported in trials of complex interventions. Despite the development of these methods, there appears to have been little widespread implementation in clinical trial practice. Therefore, this methodological PhD project will seek to provide some important advances, guidance, and direction in this field.
What the studentship will encompass:
This PhD studentship will take a 2-phase approach. Phase 1 will undertake both a scoping systematic review of proposed statistical methods to handle multiple outcomes in clinical trials of complex interventions, and a retrospective review of published RCTs of complex interventions in high impact general medical journals to assess how multiple outcomes have been handled. The findings of phase 1 will then inform a more computationally intensive 2nd phase that will include empirical simulation work, comparing/contrasting the performance of identified different statistical methods for handling multiple outcomes in individual participant data sets from completed complex intervention trials within the MRC/CSO Social & Public Health Sciences Unit (SPHSU). In addition to publication of open-access scientific papers, any new statistical code developed from the project will also be made publicly available, and disseminated to the TMRP, and UK Clinical Research Collaboration Clinical Trials Unit groups.
Prof Rod Taylor and Dr Grace Dibben will lead the supervision for the 1st project phase. Prof Taylor is a member of TMRP PhD Advisory, Strategy and Outcome theme groups and is an experienced PhD supervisor. Dr Dibben has a strong interest in clinical trial methodology and experience in methodological reviews. Supervision for the 2nd project phase will be led by Prof Ruth Dundas and Prof Richard Emsley. Prof Dundas has methodological interests in the evaluation of complex interventions and is an experienced PhD supervisor. Prof Emsley is co-lead of the TMRP Statistical Analysis Working Group, has a strong interest in the application of statistical methods for complex interventions, and is an experienced PhD supervisor. The MRC/CSO SPHSU has a rich cohort of PhD students and an excellent track record of supporting and training students. The student will be encouraged to participate in a range of unit events, meetings, and activities, and have plenty of opportunities to showcase their work and engage with other students and researchers.
Although we do not anticipate industry placements or field work, the project will include a placement opportunity for the student to spend time with Professor Emsley’s Trials Methodology Research Group at Kings College London to develop their statistical analysis skills and gain experience to deliver the 2nd project phase.
The ideal candidate for this post will have a good undergraduate degree in an area of quantitative science (e.g. maths/statistics); they may also have Masters degree within a relevant field (e.g. medical statistics, epidemiology, population health science), and a keen interest in applying their skills to complex intervention trials within public health.
HOW TO APPLY
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]