Care Professionals & Postgraduates

Medics, paramedics, general practitioners and policy makers can all come to the Julius Center for further training or refresher courses.  We offer master classes and study programs in Public Health, Epidemiology and Biostatistics. Our division also provides refresher courses for general practitioners, the courses of Cochrane Netherlands and the Executive Master of Business Innovation & Entrepreneurship in Health.

Cochrane Netherlands uitklapper, klik om te openen

Cochrane is an independent international organization. Our mission: to help healthcare professionals, policymakers and patients take decisions on medical treatments or diagnostic tests.

We do this by making information on the effectiveness of healthcare accessible in the form of systematic literature reviews. These Cochrane Reviews are published online in The Cochrane Library.

Cochrane Netherlands is one of the Cochrane Centers in the world that represent Cochrane's work. We provide methodological advice, draw up systematic reviews and provide training for postgraduates and healthcare professionals.

Post-academic education for general practitioners uitklapper, klik om te openen

The Post-Academic Education for General Practitioners (PAO-H) is part of the Department of General Practice. Every year, the PAO-H organizes refresher courses for general practitioners and trainee general practitioners. The objective is to enhance the knowledge and experience of Dutch general practitioners in pre-determined indication areas.

Data Science and Biostatistics uitklapper, klik om te openen

The Department of Data Science and Biostatistics is a multidisciplinary team that uses our professional knowledge to improve the quality of research. We offer, among others, statistics courses for students of the faculties of Medicine, Veterinary Medicine, Biology and Pharmaceutical Sciences. We also offer specialized postgraduate statistics education to scientists, focusing on the principles and methods of data science and biostatistics.

Biostatistics for Researchers (part-time)

This course provides an introduction into statistical methodology for life sciences and discusses a number of statistical techniques for practical data analysis. 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)within the Netherlands 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 send an e-mail to biostat@juliuscentrum.nl .

Biostatistics for Researchers (full-time, via Utrecht Summer School)

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.

Survival Analysis (Utrecht Summer School)

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.

Causal Inference (Utrecht Summer School)

(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.

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