Short courses - MRC Biostatistics Unit
Overview
We run a number of successful online and in-person courses in statistics on a range of topics at different levels, for statistical, clinical and other audiences.
Online courses take place through the University of Cambridge’s Moodle platform, while in-person courses normally take place in Cambridge at the East Forvie Building on the Cambridge Biomedical Campus.
Information about our upcoming courses is included below.
Upcoming BSU short courses
Information about the courses we will be running in the future and how to register.
Bayesian statistics is an approach to data analysis, wherein prior beliefs about a certain quantity of interest can be formally incorporated and updated when new data become available. There is a big role for Bayesian statistics to play in making clinical trials more efficient. This two-day short course is designed to provide an introduction to both basic aspects and advanced features of Bayesian methods with applications to clinical trials. Day 1 will be a primer in Bayesian design and analysis of clinical trials, including fundamentals of Bayesian statistics, prior elicitation, and practical implementation of Bayesian designs in statistical software. Day 2 will focus on Bayesian adaptive methods for clinical trials, historical information borrowing, and basket trial designs. The course will be a mix of lectures and practical session during which the attendees will apply learned methods in problems motivated by real clinical trials.
Course tutors
- Dr Pavel Mozgunov
- Prof Thomas Jaki
- Dr Libby Daniells
Intended audience
Statisticians from academia (clinical trial units and methodological department) and industry (pharmaceutical companies, biotechs, CROs)
Dates
Tuesday 22 and Wednesday 23 September 2026
Location
MRC Biostatistics Unit, East Forvie Building, Forvie Site, Robinson Way, Cambridge CB2 0SR
Registration
This online short course introduces students to Bayesian statistical methods in biomedical settings, and provides skills for designing, assessing and interpreting Bayesian analyses using the R and JAGS statistical software. The emphasis throughout will be on practical, applied modelling: code to carry out analyses will be provided.
The course runs on the Moodle online learning platform, and involves 7 sessions:
1. Quantifying uncertainty with probability
2. Bayesian inference
3. Bayesian regression models
4. Critiquing and comparing Bayesian models
5. Hierarchical models
6. Modelling with missing and censored data
7. Integrating multiple sources of data
The course consists of 7 half-days worth of content, and will take place over 7 half-days across 3 weeks starting on Friday 6th November 2026. There will be some on-demand content and some timetabled (live) sessions.
Course tutors
- Dr Anne Presanis
- Dr Robert Goudie
- Dr Christopher Jackson
Intended audience
Statisticians and people doing statistical analysis in any subject area.
Dates
6 - 21 November 2026 - Online
Registration
