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MRC/CSO Social and Public Health Sciences Unit - Complexity in Health programme

MRC/CSO Social and Public Health Sciences Unit - Complexity in Health programme

Our Complexity in Health programme develops and applies research methods that are designed for understanding the variety of interdependent factors that shape the impact of interventions and policies that aim to improve health and to reduce health inequalities. For studentship topics available in the Complexity in Health programme, see below. To view studentship opportunities for our other research programmes, visit our website.

Please note that topics are indicative. Student-led applications/topics relevant to the Unit and Programmes (i.e. on related topics with different supervisors) are also very welcome.

Handling multiple outcomes in complex intervention trials

Lead supervisor: Prof Rod Taylor
In the majority of randomised trials, multiple outcomes are of interest. Traditionally, the analysis and interpretation of trials focuses on a pre-defined ‘primary outcome’ and, in parallel, consideration of the impact on other ‘secondary outcomes’. This studentship is offered to explore the methodological issue of how to better handle multiple outcomes collected in the context of complex intervention trials. Can the design and analysis of trials of interventions with multiple outcomes be improved? What are the relative merits of alternative strategies such as composite outcomes, multiple response models, and multivariate approaches to analysis? The methods for this studentship are likely to include: systematic review and meta-epidemiology including individual data meta-analysis and simulation and will be under the umbrella of the MRC-NIHR Trials Methodology Research Partnership.

Agent based models in population health

Lead supervisor: Dr Eric Silverman
Many population health issues are driven by interacting behavioural, environmental and social factors. Agent-Based Models (ABMs) allow us to examine how multiple, complex patterns of interactions between individuals, their health behaviours and their surrounding environment, combine to determine population patterns in health outcomes. We are interested in supervising PhD students to either (i) develop ABMs to assess the health outcomes of welfare reforms (such as the Universal Basic Income), or to investigate the provision of social care; or (ii) develop procedures and software tools for the application of machine learning and deep neural networks to the analysis of ABMs.

Improving the quality of life, activity, connectedness and mental health of older adults living in a community setting

Lead supervisor: Prof Sharon Simpson
Social isolation and loneliness among older people are linked with many health problems and poorer mental health. There is some evidence from systematic reviews that interventions offering social activity and/or support within a group format can be effective and social network approaches to understanding and tackling loneliness and social isolation show promise. Systems approaches may also offer potential for understanding the multiple influences on our behaviour, as well as addressing change at a broader level. This PhD will explore the evidence on the relationship between loneliness/social isolation and health and consider how interventions involving social networks and social support might address these issues. The PhD is likely to involve qualitative or mixed methods and could include a systematic and/or theoretical review as well as collecting primary data through, for example, systems mapping and stakeholder workshops.

How to apply

Candidates are required to prepare a two A4 page research proposal. Please contact the supervisor of your proposed topic to discuss your proposal prior to submission.

Applications should be submitted to Postgraduate Admissions. Please ensure you apply to MVLS - MRC/CSO PhD Studentship.

The full set of supporting documents that are required to be uploaded at the point of application can be found here

  • CV/Resume
  • Degree certificate (if you have graduated prior to 1 July 2015)
  • Passport
  • Two A4 page research proposal (This should have been discussed with the Programme Leader/supervisor prior to submission).
  • Reference 1 (a full reference should be submitted from an academic who has a knowledge of your academic ability from your most recent study/programme)
  • Reference 2 (a full reference should be submitted from an academic who has a knowledge of your academic ability)
  • Transcripts

Full eligibility criteria is available here.

Once you have submitted your application, please email [email protected] to confirm. General enquiries regarding the application process can also be directed to this email address.

Closing Date: 24 February 2020
Interviews: 21 and 23 April 2020

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