Bayesian methods in biomedical research
Graduate School of Health and Medical Sciences at University of Copenhagen
This is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH.
Anyone can apply for the course, but if you are not a PhD student at the Graduate School, you will be placed on the waiting list until enrollment deadline. After the enrolment deadline, available seats will be allocated to the waiting list.
The course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD students at NorDoc member faculties. All other participants must pay the course fee.
Learning objectives
A student who has met the objectives of the course will be able to:
1. understand and assess a Bayesian modelling strategy, and discuss its underlying assumptions
2. rigorously describe expert knowledge by a quantitative prior distribution
3. perform a Bayesian regression using R, applied to meta-analysis
4. put into perspective the results from a Bayesian analysis described in a scientific article
Content
Bayesian analysis is a statistical tool that is becoming increasingly popular in biomedical sciences. Notably, Bayesian approaches have become commonly used in adaptive designs for Phase I/II clinical trials, in meta-analyses, and also in transcriptomics analysis.
This course provides an introduction to Bayesian tools, with an emphasis on biostatistics applications, in order to familiarize students with such methods and their practical applications. Thanks to its rich and flexible modelling possibilities and intuitive interpretation, the Bayesian framework is appealing — especially when the number of observations is scarce. It can adaptively incorporate information as it becomes available, an important feature for early phase clinical trials.
For example, adaptive Bayesian designs for Phase I/II trials reduce the chances of unnecessarily exposing participants to inappropriate doses and have better decision-making properties compared to the standard rule-based dose-escalation designs. Besides, the use of a Bayesian approach is also very appealing in meta-analyses because of: i) the often relatively small number of studies available, ii) its flexibility, iii) and its better handling of heterogeneity from aggregated results.
Thanks to modern computing tools, practical Bayesian analysis has become relatively straightforward, which is contributing to its increasing popularity. JAGS is a flexible software interfaced with R, that allows to easily specify a Bayesian model and that automatically perform inference for posterior parameters distributions as well as graphic outputs to monitor the quality of the analysis.
The aim of the course is to provide insights into Bayesian statistics in the context of medical studies. We will cover the following topics:
1) Bayesian modeling (prior, posterior, likelihood, Bayes theorem);
2) Bayesian estimation (Credibility Intervals, Maximum a Posteriori, Bayes factor);
3) Bayesian applications to meta-analyses;
4) Practical Bayesian Analysis with R and JAGS softwares;
5) Critical reading of medical publications. All concepts will be illustrated with real-life examples from the medical literrature.
Participants
This course is targeted towards students in graduate programms at the Faculty of Health and Medical Sciences. To be able to follow this course, participants need both:
- some knowledge in statistics (most notably some familiarity with usual probality distributions, probability denstity functions, confidence intervals and Maximum Likelihood Estimation), and
- a practical knowledge of R programming (especially functional programming, for loops and "if" statements, vector allocation, linear regression).
An online technical introduction will be provided, briefly covering these notions to check the students qualify for the above requirements. Estimated completion time for this introduction is 3h +/- 1h (depending on your R skills and familiarity with those concepts). Completing this online introduction ahead of the course is mandatory . Advanced mathematical training is not required as we will explain the methods on an elementary mathematical level, but some familiarity with function integration could be helpful.
During the practical labs on their laptop, the students will learn how to technically apply the Bayesian tools on real data, and should be able to perform a Bayesian regression by the end of the course.
Note that several statistical software can be used for Bayesian analysis, however practical lab solutions will only be provided for the R and JAGS softwares (alternatives such as SAS, WinBUGS or STAN will not be covered).
Relevance to graduate programmes
The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH:
Biostatistics and Bioinformatics
Public Health and Epidemiology
All graduate programmes
Language
English
Form
Lectures, exercises, scientific article discussions and computer practicals. There will be online exercises for approximatelly 3 hours to be completed before the first course day.
Attendance to 80% of the classes is needed to pass the class
Course director
Paul Blanche, Associate professor, University of Copenhagen
Teachers
Boris Hejblum, Research Faculty, Inserm Bordeaux Population Health
Dates
29, 30 and 31 October 2025
All days 8-15
Course location
The Faculty of Social Sciences (also known as CSS) - located in the old municipal hospital as part of City Campus.
Registration
Please register before 28 September 2025
Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules.
Applications from other participants will be considered after the last day of enrolment.
Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.