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We have 33 Medical Statistics PhD Projects, Programmes & Scholarships PhD Projects, Programmes & Scholarships

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Medical Statistics PhD Projects, Programmes & Scholarships PhD Projects, Programmes & Scholarships

We have 33 Medical Statistics PhD Projects, Programmes & Scholarships PhD Projects, Programmes & Scholarships

A PhD in Medical Statistics will require you to provide expert statistical inputs to issues in medical health research. You’ll be concerned with either applying existing or developing new statistical methods in areas of medicine like public health, clinical trials or epidemiology.

What’s it like to do a PhD in Medical Statistics?

Statistics has a major role to play across medicine and public health. Research projects in Medical Statistics have both components of Statistics and Medicine. As a Medical Statistics PhD student, you can think of your project as a combination of research in statistical methodologies and their application to challenges in medicine and public health.

Some popular statistical methods in Medical Statistics include

  • Bayesian statistics
  • Casual inference
  • Computational analysis

Some popular medical applications in Medical Statistics include

  • Bioinformatics
  • Brain imaging
  • Clinical trials
  • Epidemiology
  • Genetics

You could also be working on the design and analysis of clinical trials or epidemiological studies. Whatever your research topic, you can expect to be dealing with large health data sets on a regular basis.

The aim is for you to be able to produce a thesis with unique and significant contributions to the field by the end of your PhD. Your thesis should be 75,000-80,000 words long to be defended in an oral examination.

In the UK, a full time PhD in Medical Statistics lasts 3-4 years. Most PhD programmes are completely research led, however, given the interdisciplinary nature of Medical Statistics there might be an induction period at the beginning of your programme during which you’ll be asked to attend basic training and go through some teaching modules.

Entry requirements

For a PhD in Medical Statistics, you’ll need to hold a First or a 2.1 Honours degree in Maths, Statistics or a related subject. A Masters, with Merit or Distinction, in these subjects is also a good foundation for a PhD in Medical Statistics. You’ll be expected to already have knowledge of both practical and theoretical elements of Maths and Statistics.

Depending on where you study, you might also have to prove you have a certain level of proficiency in the language of instruction at your university.

PhD in Medical Statistics funding options

In the UK, a PhD in Medical Statistics is funded by the Medical Research Council (MRC) which provides fully funded studentships along with a tax-free stipend and an annual travel and training budget.

If your PhD comes with an MRC studentship attached, you’ll get guaranteed funding provided you are successful in your application.

PhD in Medical Statistics careers

Many Medical Statistics doctoral graduates chose to either continue research or join academia. However, with the skills and knowledge you’ll gain during your programme, you can also look at a career at public health organisations like the NHS or pharmaceutical companies.

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Are macrophages the heroes or villains of infection control?

The field of host pathogen interactions has never been more important. From the recent Covid19 pandemic to the causes of cancer and neurodegenerative disease host pathogen interactions underpin the pathology of many important diseases. Read more
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Global Doctoral scholarship – St Andrews and Bonn – Medicine

The University of St Andrews and the University of Bonn are pleased to offer a scholarship funded by both institutions, to support an exceptional student undertaking doctoral research in the following area. Read more
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Integrating protein data into large international resources to address clinical challenges in prostate cancer

  Research Group: Statistical Medicine and Molecular Biology
The University of St Andrews and Macquarie University are pleased to offer a scholarship funded by both institutions, to support an exceptional student undertaking doctoral research in the following project. Read more

Predicting response to breast cancer therapies using integrative multiplatform profiling

Breast cancer continues to have a high mortality rate, which can in part be explained by the fact that patients are treated with ‘one size fits many’ therapies that are agnostic to the molecular complexity of the tumour ecosystem (Rueda and Sammut et al., Nature 2019). Read more
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Comprehensive multi-omic analyses of “islands of resistance” tumour cells to identify aromatase inhibitor resistant subclones in oestrogen receptor positive breast cancer

Over 80% of breast cancer (BC) patients in the developed western world have oestrogen receptor (ER)–positive disease; their treatment normally includes surgery and adjuvant endocrine therapy, and sometimes chemotherapy which greatly improves survival outcome (1). Read more
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Young people, urban green and blue space use, and barriers.

This studentship will investigate the current mental health crisis and explore how this is influencing urban green and blue spaces (UGBS) use in young people, as well as how urban green and blue spaces may help to mitigate poor mental health in this group. . Read more

Longitudinal trends in asthma mortality and uncontrolled asthma, and predictors for asthma-related deaths in children living in England

The 2014 National Review of Asthma Deaths (NRAD) (1) reported the detailed findings from a review of 195 asthma deaths (28 children and young people ≤ 19 years old) occurring in the UK between February 2012 and January 2013. Read more

A Multi-Omics approach to understanding cardiovascular risk in patients with End-Stage Kidney Disease

Background. End-stage Kidney failure, also known as End-Stage Kidney Disease (ESKD), is the final, irreversible stage of chronic kidney disease (CKD), where kidney function has worsened to the point that the kidneys can no longer function independently. Read more

Transforming neuro-pathology with deep learning (ref: SF22/HLS/APP/Schwalbe)

Medulloblastoma is the most common central nervous system tumour of childhood. Although advances in treatment have raised survival to ~75%, there remains significant numbers of patients who die of their disease. Read more

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