MRC Biostatistics Unit – Short Courses
The MRC Biostatistics Unit run a number of successful courses in statistics on a range of topics at different levels, for statistical, clinical and other audiences. These courses normally take place in Cambridge at the East Forvie Building on the Cambridge Biomedical Campus, and are mostly either 1 day or 2 days.
Information about our courses is given below.
2022 COURSE DATES
Mendelian Randomization 31st October – 21st November 2022
The Mendelian Randomization course will be delivered remotely via our online learning platform.
Date: October/November 2022.
Week 0 = week beginning Monday 31st October
Week 1 = week beginning Monday 7th November
Week 2 = week beginning Monday 14th November
Week 3 = week beginning Monday 21st November
Registration is now open HERE.
For further course details, please see: Mendelian randomization course
Genetics in Drug Development – 28th November – 2nd December 2022
The new course on Genetics in Drug Development will be delivered remotely via our online learning platform.
Date: 28th November – 2nd December 2022
Registration is now open HERE.
For further course details, please see: Genetics in Drug Development
Bayesian Statistics Course – 28 November 2022 – 6 December 2022 (Online)
This 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:
- Introduction to Bayesian statistics
- Bayesian inference
- Bayesian regression models
- Critiquing and comparing Bayesian models
- Hierarchical models
- Modelling incompletely-observed data
- Integrating multiple sources of data
The course will be delivered via the Moodle online learning platform. The course consists of 7 half-days worth of content, and will take place over 2 weeks (5 days in week 1 and 2 days in week 2). It will consist of some on-demand content and some timetabled (live) sessions.
- The talks in the course are pre-recorded and available to watch on-demand whenever is convenient. The talks for each half day session last up to 90 minutes in total.
- The computer practical sessions (around 1-1.5 hours) can be completed whenever is convenient. Full solutions are available to participants.
Live interactive sessions:
- There is an associated live online drop-in session for each half-day to come and ask questions (1pm-2.30pm UK time each day)
- These are not compulsory, but are supplementary to the core content, and are an opportunity to engage with the course tutors.
Questions can also be asked during the week on a dedicated Slack channel.
The target audience of the course is statisticians and people doing statistical analysis in any subject area.
No experience of Bayesian methods is assumed, but we do we assume familiarity with key statistical concepts:
- Basic probability concepts: discrete and continuous random variables; probability density functions; expectation; variance; familiarity with standard probability distributions (e.g. normal, binomial, uniform).
- A good understanding of classical (ie non-Bayesian) statistical modelling: likelihood (as in maximum likelihood estimation) and sampling distributions; linear regression; generalised linear models including logistic regression; assessment of model fit using residuals.
No experience with specialist Bayesian software will be assumed, but you should be comfortable using the statistical software R:
- While all code is provided, we think that you will find it easier to follow if you have a good familiarity with R, and we may not be able to fully support users with no R experience.
- Understand the principles of Bayesian statistics: learning from data and judgements through probability distributions on parameters in models.
- Design a range of Bayesian models for health science problems, including appropriate selection of prior distributions.
- Implement the models in standard software, and assess the performance of computational algorithms used for this.
- Summarise and accurately interpret the output of Bayesian analyses.
- Understand the assumptions being made in Bayesian models, and effectively appraise and compare them both qualitatively and quantitatively using standard methods.
Dr Anne Presanis – MRC Biostatistics Unit
Dr Robert Goudie – MRC Biostatistics Unit
Dr Christopher Jackson – MRC Biostatistics Unit
Registration now open:
More Info: Bayesian Statistics
We’ll be in regular contact about all our new virtual courses and keep this page up to date. However, if you have any questions, please email: email@example.com