Note: The topics below are indicative. Student-led applications/topics relevant to the Unit and Programmes (ie on related topics with different supervisors) are also very welcome.
Developing an intervention to improve 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. 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. Methods could involve a systematic or theoretical review, co-production with key stakeholders, system mapping and qualitative or mixed methods approaches.
Application of Agent-Based Modelling 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) are a potentially promising, yet currently underused method, to understand and simulate complex population health issues. We are interested in supervising PhD students to either (i) develop an ABM on a particular issue, which may be suggested by the applicant or based on planned projects in modelling multimorbidity, Universal Basic Income policy in Scotland, or demand for social care; or (ii) develop procedures and software tools for the application of machine learning and deep neural networks to the development and analysis of complex ABMs.
Issues in the Design and Analysis of Randomised Trials of Complex Interventions
Lead supervisor: Prof Laurence Moore or Prof Rod Taylor
A studentship is proposed on two issues in the design of randomised trials of complex interventions. The first is the collection of large quantities of baseline data, often at great expense and putting recruitment and retention rates at risk. Can the cost and design of trials be improved by reducing the volume of baseline data collection? The second is: 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; multivariate approaches to analysis? Methods may include: systematic review; individual data meta-analysis; simulation.
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