Medical Statistics with R
In this course an introduction to basic statistical methods useful for biomedical data analysis will be given. Concepts are taught in an intuitive manner, alternating between short lectures and practicals. This allows for plenty of interaction and illustration with examples of practical interest. Participants who aim to use more complex methods can use the concepts and skills learned during the course as basis, as the vast majority of statistical methods are implemented in R.
Program
• Exploratory data analysis
• Basic tests: t-test, Wilcoxon test; paired versions; ANOVA (F-test), Kruskal-Wallis Power and sample size determination
• Methods for count data: Tests for 2x2 tables and nx2 tables; Relative risk, odds ratio
• Regression models: Linear and logistic regression
• Logistic regression
• Survival data analysis
Enrollment
You may only enroll when you participated in the Introduction to R course in the past or are very experienced in working with R.
Preparations
Knowledge: Participants must have elementary knowledge of statistics, such as of quantities like mean, median and standard deviation. At the beginning we will review concepts needed shortly.
Laptop: Participants must bring their own laptops capable of running R and RStudio. Please install R (from the Comprehensive R Archive Network-CRAN, for example from this mirror) and download and install RStudio before the course. Participants should also install the R package RMarkdown prior to the course. You will receive detailed instructions about how to prepare your laptop via email about 1 week prior to the course.
Who should attend
Researchers who need to run their own statistical analyses, and want to do it in a transparent and reproducible manner. While most participants tend to be PhD students and postdocs, more senior researchers can also benefit from the course.
Afterwards:
• You will be able to write R scripts
• You will understand R scripts written by others
• You will be able to use R to perform statistical analyses of own data
• You will be able to generate analysis reports using RMarkdown
Course lecturers
Renee X. de Menezes
Renaud Tissier
Vincent Pappalardo
Terry Chan
Course duration (2026)
6 days, 09.00 - 17.00 hrs
This course is not available online.
Course costs (2026)
Free: NKI or AVL researchers
€ 330.00 (PhD Students)
€ 440.00 (other researchers, postdocs etc.)
€ 860.00 (Non-academic participants)
Remarks
As we can accept a limited number of participants, we suggest those interested to register as soon as possible to guarantee a place.
NKI participants can register via the Learning Portal. Other participants should send an e-mail to secretariaat.psoe@nki.nl to register.
Note that registration for this course is independent of registration for other courses, such as “Introduction to R”. Participants with little work knowledge of R must therefore register for that course separately.
ECTs
Participants who have a minimum of 80% attendance and successfully complete the final assignment earn 2 ECTs.
No medical accreditation possible.