Statistical analyses in R for health scientists

Target group

PhD students, especially those doing research in health sciences.


Prerequisite: Basic knowledge on statistical hypothesis testing and linear regression.


Biostatistics Unit, Faculty of Medicine and Health Sciences, Ghent University, Roos Colman and dr. Ellen Deschepper

Added Value

This course will offer the unique opportunity for PhD students to learn to work with R progressively from the start with expert guidance. The sessions will be interactive and practical, making it possible for the participants to learn fast to use R in their research studies. Furthermore, in the last session the participants will gain theoretical and practical insight into specific statistical models, specifically applied to their domain of research.


The specialist course will consist of seven interactive and practice sessions. The first five sessions will be guided by 2 statistical experts of the Biostatistics Unit of the Faculty of Medicine and Health Sciences of Ghent University. These sessions will be focused on specific topics to learn the basics of R progressively from the start. Participants will be asked to engage in self-study (using the book “R for dummies”). During the sessions, specific exercises related to the domain of health sciences will be discussed. Furthermore, during the 2 last sessions, lectures will be given by dr. Jelle Van Cauwenberg (PhD student from the Department of Public Health at Ghent University) on the use of generalized linear models. During this lecture, practical tips will be shared with respect to the use of these models for specific research questions in the domain of health sciences.


The course will offer seven sessions concerning the use of R for statistical analyses in health sciences. The first five sessions will consist of highly interactive sessions during which the basics of R will be learned progressively through a combination of intense guidance by 2 statistical experts and self-study. Emphasis will be on practical skills, necessary to –in a next phase- conduct specific and advanced statistical analyses with R. The last two sessions will consist of a lecture focused on the use of R in generalized linear modeling, including hurdle and zero-inflated regression models. The theoretical fundamentals of these models and their extensions will be addressed and information will be provided on how to choose the most appropriate model. Practical skills such as how to conduct the models in R and how to interpret the results, will also be part of these sessions.


Dr. Ellen Deschepper and Roos Colman, two statistical experts, will lecture the first five sessions of this specialist course. Dr. Jelle Van Cauwenberg will lecture the last two sessions.

Dr. Ellen Deschepper and Roos Colman, both members of the Biostatistics Unit at the Faculty of Medicine and Health Sciences, provide statistical consulting and support to researchers of the faculty and UZ Gent. They are both experts in the design and analyses of clinical research in various software packages such as R, SPSS, SAS, etc. Dr. Ellen Deschepper graduated in 2000 as a Master of Science in Biostatistics at Hasselt University and obtained her Phd in Statistics in 2007 at Ghent University. She has over 15 years of experience in the field of biostatistics and was involved in several expert courses in statistics and in the practical use of statistical software. Roos Colman graduated in 2006 as a Master in Biomedical Sciences, and obtained the degree of “Master of statistical data analysis” at Ghent University in 2010.

Dr. Jelle Van Cauwenberg is a Postdoctoral Fellow at the Department of Public Health (Ghent University). During the past years he completed several courses of the Master in Quantitative Analysis in Social Sciences and the Master of Statistics at the KULeuven. These courses included ‘Statistical software’, ‘Statistical modelling’, ‘Multilevel analysis’, ‘Mixed and multilevel models’ and ‘Longitudinal data analysis’. He applied generalized linear (mixed) models (including hurdle and zero-inflated models) in R for his own research. He previously taught statistics to bachelor and master students physical education and physiotherapy at the Vrije Universiteit Brussel and he lectured several courses on multilevel modelling and generalized linear models for PhD students.  


    February 16, 17, 23 and March 9, 10, 16 and 17, 2017 from 9h till 11h30. Participants will be asked to engage in self-study (2 hours/week).


     UZ campus - K3 (entrance 42), first floor- PC room 1.1 - Faculty of Medicine and Health Sciences of Ghent University, De Pintelaan 185, 9000 Ghent, Belgium


    Date                                                       Contents

    • February 16, 2017    Exploring R  and Rstudio - Constructing and working with vectors - Reading SPSS datasets
    • February 17, 2017     Getting help - Reading excel and csv datasets  - Data manipulation: creating variables, creating subsets, sorting and ordering
    • February 23, 2017    Summarizing data - Graphics - Basic statistical tests in R
    • March 9, 2017          Linear regression models in R
    • March 10, 2017         Logistic regression models in R
    • March 16, 2017         Generalized linear models in R
    • March 17, 2017         Hierarchical generalized linear models in R


    Registration link:

    If the course is fully booked, you can send a request to

    Evaluation criteria (doctoral training programme)

    Participants are required to be present and participate actively during at least 6 out of 7 sessions. They will be asked to read and practice some questions/problems before each session.

    Registration fee

    The course will be free of charge for members of the Doctoral Schools of UGent. Participants are asked to buy the book “R for dummies”, which is extensively used during the first five sessions.


    The sessions will be in English except when all attendants are Dutch-speaking.

    Number of participants

    A maximum number of 20 participants can subscribe to allow highly interactive sessions in a pc room