PhD projects

We are one of the UK's top university departments for postgraduate research studies in the fields of health services research, health economics and public health. You may study for a research degree (PhD or MD in Health and Related Research) on a topic in these areas.

Three students sit in the library and have a discussion.

The school welcomes enquiries at any time from those who wish to study for a research degree. Interested candidates should in the first instance contact prospective supervisors to discuss their proposed research. We are happy to discuss research proposals that fall within our main research areas, and to give guidance on the scope and focus of a thesis topic.

Possible projects with identified supervisors can be viewed further down this page. These are not funded projects but are a guide to the sort of topics that supervisors might be interested in supervising.

For more general enquiries on the application process, the School of Health and Related Research (ScHARR) Postgraduate Research Administrator can be contacted via or +44 114 222 5454.

Funded project opportunities

Wellcome Trust DTC in Public Health Economics and Decision Science

Seven funded PhD places are available in our Wellcome Trust DTC in Public Health Economics and Decision Science.

Other funded project opportunities


Potential PhD projects

In the first instance, students interested in one of the projects below should contact the relevant potential supervisor. Please note there is no funding attached to any of these projects, any potential student interested would need to source their own funding.

Environmental influences on health behaviour

Supervisor: Emma Hock

The impact of the environment on health behaviour is beginning to be recognised, but there is much that is unknown. Changes to the environment (also known as ‘nudging’) have the potential to impact on people’s behaviour and also social and psychological factors and contexts.

In particular, I am interested in supervising a PhD examining the impact of building design features on people’s physical activity behaviour (and possibly on how people think and feel, and on the broader culture), although I am open to suggestions for supervising doctoral research concerning other aspects of environmental structures on other health behaviours. Within the doctoral research programme, I would envisage:

  • Observational research examining the impact of building design on physical activity in terms of movement throughout the building – largely quantitative but with scope for qualitative research to assess how people see the physical structure impacting on their activity and habits
  • Intervention/s to assess the impact of altering elements of building design to encourage physically active movement throughout the building – mixed methods, either in terms of quantitative intervention design and measurement with qualitative process evaluation, or designing intervention/s through participatory action research or social action and using quantitative and qualitative techniques to assess the impact of the intervention

Physical activity as a smoking cessation aid

Supervisor: Emma Hock

There is much experimental research to suggest that short bouts of physical activity (such as a 10-minute brisk walk) can reduce cigarette cravings in abstaining smokers, however to date research examining the impact of integrating physical activity into smoking cessation programmes has only examined formal structured exercise rather than encouraging its use in short bursts to relieve cravings.

Smoking cessation advisors within the UK Stop Smoking Service also feel that physical activity can be most beneficial if it is promoted as a cessation aid and pilot collaborative research within the UK Stop Smoking Services suggests that this is feasible, however, the effectiveness of such an intervention has yet to be fully examined.

I would be interested in supervising doctoral research examining the effectiveness of the use of short bouts of physical activity as a smoking cessation aid within the Stop Smoking Services. I would envisage the doctoral research programme consisting of a large trial or series of small trials with a qualitative process evaluation and potentially making use of collaborative action research.

Generic dimensions of barriers and facilitators to the uptake and acceptability of complex interventions

Supervisor: Andrew Booth


“Successful implementation depends on changing behaviour – often of a wide range of people. This requires a scientific understanding of the behaviours that need to change, the factors maintaining current behaviour and barriers and facilitators to change” (MRC Framework).

Carl May has distilled barriers to and facilitators of supply-side implementation to four domains described by the NPM: interactional workability of collaborative care; relational integration; skill-set workability; contextual integration.

Three concepts, barriers, facilitators and acceptability, are ubiquitous in qualitative research evaluating the uptake of therapeutic interventions by participants however action researchers generally have to establish sub-domains for each project on an ad hoc basis.

While each health care intervention and context does present unique challenges, if there were a level of abstraction at which sub-domains could be defined, it would expedite the categorisation of qualitative material by action researchers. In fact, several systems already exist, mostly outside of the health services/technology research sphere (see over).

Between some there appear to be overlap and sometimes commensurability of terms but there is no obvious standard system of exhaustive and mutually exclusive groups.


Develop a system of mutually exclusive (?), exhaustive categories of factors which relate to the uptake of complex interventions by patient groups.


  • Year 1: Multi-disciplinary literature review and survey
  • Year 2: Consultation and reanalysis of case studies. Convene sample of qualitative researchers who have produced case studies on the barriers/facilitators/acceptability of complex interventions. Delphi exercise on commensurability of terms. The researcher takes primary datasets away recodes case studies using the new system. Preparation of “dummy statements” survey for factor analysis?
  • Year 3: Consultation, factor analysis and write-up. Participating researchers answer “dummy statements” survey. New Delphi exercise on the success of the re-coding study. Write-up of results.
Healthcare communication systems

Supervisor: Susan Baxter

The way that healthcare staff share information about patients is a key element which impacts on the quality and safety of patient care. There have been a number of interventions developed in recent years such as checklists to use in operating theatres or to use when making or receiving telephone calls.

There has also been a focus in the UK on improving staff team working skills, with communication being an important part of these interventions. Recent high profile failures in care, however, indicate that there is much work still to be done to ensure optimal communication systems between hospital professionals, between hospitals and the community, and between health and social care.

There are potentially many strands of this issue that could be investigated in a PhD study including staff/patient perceptions of interventions, evaluating systems, and exploring the impact on patient outcomes.

The challenge of successful transfer of information within healthcare delivery has applicability internationally. A proposal could include a systematic review of effectiveness of interventions, a review of qualitative evidence or use one of the emerging methods for synthesising evidence such as realist synthesis or logic modelling.

Besides evidence synthesis methods primary research could use qualitative approaches or mixed-method designs.


  • Baxter, S., & Brumfitt, S. (2008). Once a week is not enough: evaluating current measures of teamworking in stroke. Journal of Evaluation in Clinical Practice, Volume 14, Issue 2, pp 241-247
  • Baxter, S., & Brumfitt, S. (2008). Benefits and losses: a qualitative study exploring healthcare staff perceptions of teamworking. Quality and Safety in Healthcare, Volume 17, Issue 2, May. 127-130
  • Baxter, S., & Brumfitt, S. (2008). Professional differences in interprofessional working. Journal of Interprofessional Care, Volume 22, Issue 3, pp 239-251
  • Baxter, S., Killoran, A., Kelly, M., Goyder, E. (2010). Synthesizing diverse evidence: the use of primary qualitative data analysis methods and logic models in public health reviews. Public Health, Volume 124, Issue 2, pp 99-106
Efficiency and practicalities of response-adaptive randomisation adaptive design in multi-arm randomised trials 

Supervisors: Dr Munya Dimairo, Professor Christina Yap, Prof Susan Todd, and Professor Steven Julious

There is an increasing demand to evaluate the benefits and safety of new treatments as quickly as possible with resources no more than necessary using a minimum number of research patients as possible. Adaptive designs use accruing data to modify aspects of an ongoing trial without compromising the validity of conclusions [1].

This allows researchers to address uncertainties that exist at the design stage and make timely decisions when there is sufficient evidence during the course of the trial (eg stopping treatment(s) or entire trial early)

Response-adaptive randomisation (RAR) is a type of an adaptive design that makes use of accrued outcome data to change randomisation allocation of patients to favour treatments that are indicating beneficial effects (away from futile or unsafe treatments).

Futile or unsafe treatments can be stopped early during the trial. In doing so, multiple treatments are investigated in a single trial and fewer patients are more likely to receive futile or unsafe treatments if beneficial treatments exist. This balances the ethical interest of research patients during the trial and research interests of investigators.

Despite the potential theoretical benefits of RAR, these adaptive designs are still at the infancy stage, and their efficiency in practice and feasibility in implementation are areas that require further investigations.

Specifically, there is a need to explore situations when certain RAR approaches are most likely to be beneficial and when it is practical to use the design. In the context of randomised trials, the objectives of this project are to:

  1. Review statistical algorithms/approaches used in RAR adaptive designs,
  2. Review and characterise RAR adaptive designs implemented in practice (eg BATTLE-2 trial [2] and Giles et al. [3]),
  3. Use simulations to investigate the efficiency of RAR methods based on results from objectives 1 and 2,
  4. Use real-life case studies of completed fixed trials to investigate practicalities in implementing of RAR methods and quantify potential benefits building on previous theoretical work [4],
  5. Create open access software resources that could be used by researchers to implement RAR approaches that are most likely to be beneficial in practice.

The project is intended to bridge the practical knowledge gap and facilitate the routine use of potentially efficient designs in randomised clinical trials research.


  1. Pallmann P, Bedding AW, Choodari-Oskooei B, et al. Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Med 2018;16:29.
  2. Papadimitrakopoulou V, Lee JJ, Wistuba II, et al. The BATTLE-2 Study: A Biomarker-Integrated Targeted Therapy Study in Previously Treated Patients With Advanced Non-Small-Cell Lung Cancer. J Clin Oncol 2016;34:3638–47.
  3. Giles FJ, Kantarjian HM, Cortes JE, et al. Adaptive Randomized Study of Idarubicin and Cytarabine Versus Troxacitabine and Cytarabine Versus Troxacitabine and Idarubicin in Untreated Patients 50 Years or Older With Adverse Karyotype Acute Myeloid Leukemia. J Clin Oncol 2003;21:1722–7.
  4. Yap C, Cheung YKK. Sequential Elimination in Multi-arm Multi-stage Selection Trials. In: Wiley StatsRef: Statistics Reference Online. Chichester, UK: : John Wiley & Sons, Ltd 2018. 1–7.

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