Reading and Writing Statistics in Clinical Publications


Course description

This course familiarizes participants with the essential statistical principles clinical publications are commonly based on.

Participants disentangle the reported statistics in a number of high-ranking publications from both experimental and observational medical studies. By learning to understand the statistical principles that underlie these diverse publications, participants acquire the skills to critically evaluate a wide diversity of statistical results that they may encounter in the clinical literature.

At the same time, the gained statistical knowledge will aid participants to plan statistical analyses for their own future studies, to interpret the results of these analyses, and to present the statistical results in a way that is correct and convincing.

Course structure

This course requires participants to attend one afternoon session a week during 6 weeks. Each session typically consists of  lectures and assignments, both performed individually and in groups.

Recommended reading

Will be announced in time before each session. 

Course dates

6x Tuesdays, 14.00 - 18.00h starting September 10, 2024. 


Sitzungszimmer Spitalstrasse 8, raised ground floor (right), Department of Clinical Research, Spitalstrasse 8, 4031 Basel 

Target audience/prerequisites for attending

PhD students of all PhD subject enrolled at the University of Basel, Faculty of Medicine; 

Early career clinical researchers and other researchers from the University of Basel, Faculty of Medicine (Biomedical Engineering, Biomedical Ethics, Medicines Development, Nursing Science, Public Health/ Epidemiology or Sport Science) are eligible to register for the course. 

Maximum 15 registered participants. Priority will be given to early career clinical researchers aiming for a habilitation. 

Learning objectives:

Upon successful completion of this course, participants will be able to 

  1. Understand the different levels of evidence.  

  2. Formulate scientifically sound hypotheses. 

  3. Choose appropriate study designs. 

  4. Understand the importance of statistical analysis plans and selection of appropriate measurements. 

  5. Understand hypothesis testing, its specific techniques, and underlying assumptions. 


Work load and credits

Approximately 60 hours of workload distributed over 12 weeks.  

The crediting of 2 ECTS requires: 

  1. Completed pre-assignments. 

  2. Active participation in weekly discussions. 

  3. Completed homework assignments. 

Attendance to all but two sessions and full completion of assignments is required for acquiring a certificate of attendance. 

Note for PhD students: The workload of this course will be tranfered into ECTS credits automatically if the course is listed in your approved learning agreement.

Assessment format

To add this course to your academic record, please create a Learning Contract on MOnA, choosing: 

  • your supervisor as the assessor 

  • pass/fail as assessment 

before the start day of the course and follow the instructions of your respective faculty. 

Study direction

Andrea Kiemen, PhD
Training and Education, Department of Clinical Research 

Teaching staff

Marco Cattaneo, PhD, Senior Statistician, Department of Clinical Research 
Gilles Dutilh, PhD, Senior Statistician, Department of Clinical Research 
Nikki Rommers, PhD, Statistician, Department of Clinical Research 


Mareike Gräter
Training and Education, Department of Clinical Research


1200 CHF

Note: The Department of Clinical Research (DKF) offers training grants for local members of DKF clinical research groups. Applications for training grants can be submitted when registering for the program and will result in a 50% reduction of the course fee. PhD students in Clinical Research can use their DKF grant and attend free of charge. Please get in touch with us (


Course language is English.
Certificate of attendance will be issued after successful course completion


Please click on the following link to register for the course: Registration