Education

(Dutch)

Within this curriculum, statistics is part of the data science “leerlijn” (combined with a substantial introduction into bioinformatics).

Throughout the first two years, mainly condensed in three blocks, students will learn about methodology (including research designs) and about descriptive and inferential statistics. The focus is on helping students to develop their statistical literacy, reasoning and thinking, rather than providing a cookbook of different tests.

At the end of their program, students will be able to choose, conduct and interpret a number of tests (t-tests, linear regression, chi-square test) and to recognize situations that need more advanced analyses (one-way, two-way and repeated measures ANOVA, multiple and logistic regression and time to event analyses). Students will learn to perform the statistical analyses in R.

(English)

Within this program, we offer an elective course (“Basics of Biostatistics”) that covers several statistical techniques that are relevant for practical biomedical data analysis. We teach the concepts of statistical estimation (point and interval estimation) and testing. The focus is on methods developed for categorical data, in particular binary data, and quantitative data, in particular normally distributed data. The course covers, simple linear regression, correlation, one way analysis of variance, analysis of contingency tables and non-parametric statistics. Furthermore, it introduces (multiple) linear regression and (multiple) logistic regression.

(Dutch)

Our teaching in statistics in the bachelor program of Medicine is primarily embedded in the course Klinisch Wetenschappelijk Onderzoek’ (KWO). This course gives students an in-depth understanding of the link between clinical practice and (clinical) scientific research. It teaches students the importance of well-conducted scientific research, data analysis and how its results can improve clinical practice.

During the statistical part of this course, we teach students the principles of statistical inference. They learn about confidence intervals, hypothesis testing, contingency tables (i.e., chi-square test, relative risk, odds ratio), comparing group means (i.e., t-test), and correlations. We also provide a practical where the students learn how to use the statistical program SPSS to analyze their data. At the end of the course, the students are able to formulate and test hypotheses and to correctly interpret the results.

(Dutch)

Clinical Health Sciences is a Dutch-language parttime study with a premaster and master program. In the premaster program we teach two courses: ‘Introductory Statistics’ and ‘Classical Statistical Methods’, in the master program we teach within the course ‘Methodology and Statistics’. In these courses students are taught the fundamentals of frequentist statistics and are given a sound basis in understanding and application of these methods in their research practice.

(English)

We teach several core courses (Introduction to Statistics, Classical Methods in Data Analysis, and Modern Methods in Data Analysis) for the Master of Epidemiology, as well as a number of specialization courses for the Medical Statistics track (Clinicals Trials and Drug Risk Assessment, Computational Statistics, Generalized Linear Models, Inference and Models, Machine Learning & Application in Medicine, Mixed Models, and Survival Analysis).

More information on the program can be found here.

(English)

This master program trains students to work as statisticians, becoming researchers, lecturers or consultants. We teach the courses ‘Computational Statistics in R’ and ‘Introduction to Biomedical Statistics’, give a consultancy workshop and mentor students during their master thesis.

More information on the program can be found here.

(Language: English; Programming language: R or SPSS)

The course is offered several times a year, either face-to-face (10 class days in 4 weeks) or online (11 weeks).

Intended audience: PhD students, biomedical researchers, and professionals (national and international) seeking to improve their statistical knowledge and enhance the quality of their medical research.

For more information and to sign up for this course, please click here.

(Language: English; Programming language: R or SPSS)

This two-week, full-time course provides an introduction in statistical methodology and covers a number of statistical techniques for practical data analysis. It is offered as part of the Utrecht Summer School, and affordable housing is available for the duration of the course.

Intended audience: PhD students, biomedical researchers, and professionals (national and international) seeking to improve their statistical knowledge and enhance the quality of their medical research.

For more information and to sign up for this course, please click here.

(Language: English; Programming language: R)

This one-week, full-time course briefly reviews, and then extends, the concepts of survival analysis, covering different types of censoring and truncation mechanisms, the Cox model and parametric survival models, time-varying covariates and competing risk analysis.

Intended audience: PhD students, biomedical researchers, and professionals (national and international) with a solid basis in statistics and data analysis (regression modelling, general linear model) and minimal experience in survival analysis who wish to broaden and deepen their understanding of modeling time-to-event data.

For more information and to sign up for this course, please click here.

(Language: English; Programming language: R)

In this one-week in-person course, participants will learn about the latest methods in causal inference with observational data, including: potential outcomes; DAGs and causal graphs; target trial emulation; causal structure learning; quasi-experimental methods; and a variety of methods for handling confounding.

Intended audience: Data science professionals and academic researchers with a background in health, social and/or behavioral science, or broad training in applied data science. The course is aimed at advanced master level and above. Participants are expected to have a solid basis in statistics and data analysis (regression modelling, general linear model) and working knowledge of R. No prior experience of causal inference techniques is necessary..

For more information and to sign up for this course, please click here.