Missing Value Imputation

Lecturers Prof. Stef van Buuren, Netherlands Organization for Applied Scientific Research TNO, Utrecht University
Dr. Gerko Vink, Utrecht University, NL, Columbia University, NY, USA
Certificate Confirmation of participation

Novice and advanced R users from all professional groups.

  • CHF 600.- for members of UZH/ETH and associated institutes
  • CHF 800.- for alumni of UZH/ETH, members of other universities, federal and cantonal research facilities and agencies, non-profit organisations
  • CHF 1200.- for companies

Nearly all data analytic procedures in R are designed for complete data and fail if the data contain NA's. Most procedures simply ignore any incomplete rows in the data, or use ad-hoc procedures like replacing NA with the "best value". However, such procedures for fixing NA's may introduce serious biases in the ensuing statistical analysis.

Multiple imputation is a principled solution for this problem. The aim of this course to enhance participants’ knowledge in imputation methodology using R. The course will explain the principles of missing data theory, outline a step-by-step approach toward creating high quality imputations, and provide guidelines on how the results can be reported.

The course will be based on the popular R package MICE. Familiarity is required to basic statistical concepts and techniques (such as regression) and the concept of statistical inference. This course will emphasize computational techniques, but no prior programming experience with R is needed.

For all Zurich R Courses participants should bring their own laptops to the course and will be informed by email in advance which packages they need to install.


February 23-24, 2017
Registration deadline: 23.01.2017


  After registering you will receive a short automatic confirmation by email. If you received this email you are successfully and bindingly registered for the course. For administrative reasons the written invoice won't be sent out until about two weeks before the course.