MSc in EBHC Medical Statistics

MSc in EBHC Medical Statistics

This is a course for health professionals who wish to strengthen their statistical skills and ability to analyse data. Students will gain the confidence in carrying out the methods that are widely used in medical statistics, and interpreting the results for the practice of evidence-based health care.
  • Gain competence in execution and interpretation of core statistical techniques used by medical statisticians inside and outside the context of clinical trials, particularly those used in multi-variable analyses: multiple linear regression, logistic regression, and survival modelling; statistical analysis plans and statistical reporting
  • Gain competence in execution and interpretation of student-selected areas: meta-analysis, systematic reviews, big data epidemiology, statistical computing, diagnosis and screening, study design and research methods
  • Gain hands-on experience, supervised by a senior member of our medical statistics team, of the analysis/meta-analysis of healthcare data, in order to address a question in evidence-based healthcare

For more information on this course, please visit the Department of Continuing Education website.

Applying for an MSc course

Applications for these courses should be made via the University of Oxford Graduate Admissions website. This website included further information about the courses we offer and a guide to applying.

Early applications are advised. Applications which have not been fully completed before the application deadline cannot be considered, so please ensure that any applications are received by us in good time so that we may advise of missing or incorrectly completed elements. Shortlisted applicants will be invited to a telephone interview and asked to provide evidence of their funding for the programme.

Looking for something else?

The programme is delivered in conjunction with the University of Oxford’s Nuffield Department of Primary Care Health Sciences, the UK’s top-ranked department in this field (Research Excellence Framework 2014) and is supported by the CEBM.

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