This is the 2023 website for the course Advanced Statistical Topics in Health Research A held by the University of Copenhagen.

Practical information

Additional information will be given on the first day.

We will be following chapters in Computer Age Statistical Inference: Algorithms, Evidence, and Data Science somewhat closely. A pdf-copy of the book can be downloaded from the author’s website. See the notes for more information.

Learning objectives

Many modern research projects collect data and use experimental designs that require advanced statistical methods beyond what is taught as part of the curriculum in introductory statistical courses. This course covers some of the more general statistical models and methods suitable for analyzing more complex data and designs encountered in health research such as methods for high-dimensional data, classification, imputation, and dimension reduction.

The course will contain equal parts theory and applications and consists of four full days of teaching and computer lab exercises. It is the intention that the participants will have a thorough understanding of the statistical methods presented and are able to apply them in practice after having followed the course. This course is aimed at health researchers with previous knowledge of statistics and the computer language R who need of an overview about appropriate analytical methods and discussions with statisticians to be able to solve their problem.

A student who has met the objectives of the course will be able to:

Laptops and software

You must bring a laptop as we will not have access to the computer rooms at the university.


Before the course starts you should make sure that you have installed the latest version of:

  • R

  • R Studio is also highly recommended but is not necessary.

  • The following R packages: mice, pls, lme4, glmnet, grf, data.table, randomForest, party, randomForestSRC, dataMaid, naniar, ranger, Matching, ggplot2, rms, riskRegression and sandwich. This list will be updated as we get closer to course start, so be sure to check back and rerun the line below before the course starts. This can be done from inside R using the following command:

    install.packages(c("pls", "glmnet", "lme4", "grf",
                       "data.table", "randomForest", "party",
                       "randomForestSRC", "ranger", "mice", 
                       "dataMaid", "naniar", "bootstrap", 
                       "Matching", "ggplot2", "sandwich",
                   "rms", "riskRegression"))

Installation instructions are available on the pages above.

There will be wireless internet access for the participants. If you already have an eduroam account then it will work throughout University of Copenhagen.

Map of CSS, UCPH

Last updated 2023