Medical Statistics

The first two semesters of your course will consist of taught modules. In the third semester you’ll devote your time to a major dissertation in statistics or a research project in applied epidemiology and biostatistics. Within each semester you have the opportunity to choose from a range of optional modules, allowing you to specialise in the area of study of most interest to you.

You’ll be taught by experts from the School of Mathematics, The Centre for Epidemiology and Biostatistics, and The Clinical Trials Research Unit at Leeds, each bringing a different perspective to the subject of medical statistics.

You’ll be supervised for both your taught modules and your research project by professionals across the teaching units and you will be given the opportunity to utilise existing links with individual clinicians and medical research groups in the University of Leeds, Leeds NHS trust, and the Department of Health’s Information Centre in Leeds.

Throughout the course you’ll learn about new developments in statistics and be provided with the opportunity to undertake data analysis for a wide variety of statistical problems. You’ll build an appreciation of theoretical and practical perspectives on issues in medical statistics, whilst developing the ability to select and apply appropriate statistical methods for the analysis of medical data using suitably chosen software packages.

Course structure

These are typical modules/components studied and may change from time to time. Read more in our Terms and conditions.

Modules Year 1

Compulsory modules

• Introduction to Clinical Trials 15 credits
• Introduction to Health Data Science 15 credits
• Modelling Prediction and Causality with Observational Data 15 credits
• Statistical Computing 15 credits

Optional modules

• Research Project 60 credits
• Further techniques in Health Data Analytics 15 credits
• Professional Skills for Health Data Analysts 15 credits
• Modelling Strategies for Causal Inference with Observational Data 15 credits
• Latent Variable Methods 15 credits
• Independent Skills in Health Data Analytics 15 credits
• Linear Regression and Robustness 15 credits
• Statistical Theory 15 credits
• Multivariate Analysis 10 credits
• Time Series 10 credits
• Bayesian Statistics 10 credits
• Generalised Linear Models 10 credits
• Introduction to Statistics and DNA 10 credits
• Linear Regression, Robustness and Smoothing 20 credits
• Multivariate and Cluster Analysis 15 credits
• Time Series and Spectral Analysis 15 credits
• Bayesian Statistics and Causality 15 credits
• Generalised Linear and Additive Models 15 credits
• Independent Learning and Skills Project 15 credits
• Dissertation in Statistics 60 credits