University of Leicester

What you'll study

The programme provides intensive training in statistical theory and its application to medical and health-related data. Core topics cover probability and statistical inference, regression modelling, survival analysis, longitudinal data analysis, and the design and analysis of clinical trials. You will also study practical statistical computing and data management, using industry-standard software and reproducible workflows.

• Statistical theory and inference — foundations of probability, estimation and hypothesis testing with emphasis on applications.
• Regression and multivariable methods — linear and generalized linear models, model selection and diagnostic methods for observational data.
• Survival and event-history analysis — censoring, Kaplan–Meier methods, Cox proportional hazards and extensions for complex time-to-event data.
• Longitudinal and repeated measures — mixed-effects models, marginal models and methods for handling correlated observations.
• Clinical trials methodology — trial design, randomisation, sample size, interim monitoring and analysis of trial outcomes.
• Bayesian methods and advanced modelling — introduction to Bayesian inference and hierarchical modelling for complex biomedical problems.
• Statistical computing and data science skills — practical training in R and other common packages for data cleaning, visualisation and reproducible analysis; programming for large datasets.
• Research project / dissertation — an extended piece of independent applied or methodological research using real medical datasets, often undertaken in collaboration with clinical or public health partners.

The programme is typically delivered through lectures, computer labs, seminars and project supervision. Assessment is by a combination of coursework, practical analysis assignments, examinations and the MSc dissertation.

Entry requirements

Applicants are usually expected to hold a good honours degree (or equivalent) with substantial quantitative content, for example mathematics, statistics, biostatistics, epidemiology, or a numerate biomedical science. Candidates with a lower degree classification but with strong practical experience in data analysis or proven quantitative skills may be considered. Applicants whose first language is not English will need to meet the university's English language requirement.

Relevant professional experience in healthcare, clinical research or statistical programming can strengthen an application. Short preparatory modules or self-study in calculus, linear algebra and introductory statistics are recommended for applicants who lack recent mathematical training.

Career prospects

Graduates go on to roles across academia, the NHS, public health agencies, the pharmaceutical and biotechnology industries, contract research organisations (CROs), and charities. Typical job titles include:

• Medical statistician / biostatistician
• Statistical programmer
• Clinical trials statistician
• Epidemiologist or public health analyst
• Data scientist or health data analyst
• Research fellow in methodological or applied health research

The combination of methodological grounding and practical computing experience prepares graduates for work on clinical trials, observational studies, health technology assessment, and regulatory submissions, as well as further doctoral study in biostatistics or related fields.

Why study at University of Leicester

The University of Leicester has a strong research base in population health and clinical science, offering students opportunities to work with researchers across medicine, epidemiology and public health. Teaching emphasises real-world application: many modules use genuine clinical and public health datasets and the department maintains links with NHS trusts and regional research networks.

Students benefit from experienced academic staff with active research portfolios in biostatistics and applied health research, hands-on computing facilities and support for statistical software training. The MSc's dissertation component encourages collaboration with external partners, helping students build contacts and practical experience relevant to careers in medical statistics and clinical research.