Introduction to Bayesian Methods in Clinical Research [ESP68]
Monday to Friday (5 full days)
Prof. Emmanuel Lesaffre
Erasmus MC, Rotterdam NL
The participants should have:
- A good statistical knowledge and practical experience with regression models: linear, binary and survival regression
- Good to extensive practical experience with programming in R
It is recommended that participants have knowledge and experience with mixed models.
Detailed information about this course:
Faculty: Prof. Emmanuel Lesaffre, PhD
The Bayesian approach is an important alternative to the classical (called frequentist) approach to statistics. Indeed, the Bayesian approach has become increasingly important over the last three decades and is invading in all application areas. Especially with complex data the Bayesian approach has proven to be a very useful analysis tool, but also conceptually this approach is attracting recently many researchers.
The course introduces the participant to Bayesian methods for the analysis of clinical and epidemiological studies. While some math cannot be avoided, the emphasis in the course is on bringing over the intuitive ideas and the analysis of clinical and epidemiological data sets using Bayesian software. The course treats the basic concepts of the Bayesian approach, such as the prior and posterior distribution and their summary measures, the posterior predictive distribution. In addition, Bayesian methods for model selection and model evaluation will be treated. Markov Chain Monte Carlo techniques are introduced and exemplified. A great variety of clinical and epidemiological examples illustrates the techniques.
Medical publications are explored in discussion groups, as well as Bayesian analyses of real data sets will be exercised on an individual and on a group basis.
- Understand the Bayesian concepts
- Understand clinical and epidemiological papers that make use of the Bayesian approach.
- Appreciate the impact of the Bayesian approach on clinical and epidemiological research
- Write an OpenBUGS program and make use of R2OpenBUGS for some basic statistical models
The participant profile includes any of the following type of researchers: clinical researchers, clinical epidemiologists, decision scientists, public health researchers, those in health technology assessment or value-based healthcare, but with the restriction that they should have a sound (see above) statistical knowledge and experience.
Reduction on fees
PLEASE NOTE: This does not apply to the fee of the research master programmes (120 EC points)
No fees are charged for Erasmus MC PhD candidates, provided they have an account in Hora Finita, the Erasmus University PhD registration system.
In case of cancellation or no show, the cancellation policy applies based on the full course fee.
25% reduction for all (international) PhD candidates without formal appointment at Erasmus MC
Upon receipt of your application you will receive a request to upload proof of enrollment as a PhD candidate.