Advanced Statistical Topics in Health Research

This is the 2025 website for the course Advanced Statistical Topics in Health Research held by the University of Copenhagen. This website serves as your central hub for all course-related information, resources, and materials.

Schedule

Please refer to the schedule below for an outline of topics covered each day. This schedule is subject to change, so be sure to check back regularly for updates.

Day 1	Multiple testing, linear models and regularization methods
Day 2	Causal discovery
Day 3	Network analysis
Day 4	Random forests
Day 5	Analysis with missing data

We will meet at CSS (Kommunehospitalet), and we will be in room 7.0.08 (building 7, ground floor, room 08) except on Tuesday where we will be in room 2.0.30 before lunch and 2.0.24 after lunch (see map at the end of this document).
The course will generally run every day from 8.15 until around 15. The format of the individual days might vary slightly so be prepared to be flexible as possible.
Please install the necessary software before the course start. See below.
We will use Computer Age Statistical Inference: Algorithms, Evidence, and Data Science as inspiration but will only cover some of the chapters. A pdf-copy of the book can be downloaded from the author’s website.
Teachers for the course will be Anne Helby Petersen, Tobias Ellegaard Larsen, Christine Winther Bang Mark Bech Knudsen, Helene Rytgaard, and Claus Thorn Ekstrøm.

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:

Analyze data using the methods presented and be able to draw valid conclusions based on the results obtained.
Understand the advantages/disadvantages of the methods presented and be able to discuss potential pitfalls from using these methods.

Software

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

R and R Studio is also highly recommended but is not necessary.
We will be using a few specialized R packages that you need to install prior to arriving. Those are necessary for us to run the exercises. The code below should be run in R (e.g. by opening RStudio and copying them into the console) to install the packages that we will need. You need to be connected to the internet to install the packages. If you are a Windows user, you may need to run RStudio as an administrator in order to install the packages. This can be done by right-clicking the RStudio program icon and choosing “Run as administrator”.

First you should run
```
install.packages(c("BiocManager", "devtools", "pdftools", "dagitty", "tinytex", "remotes", 
                   "glmnet", "gglasso", "selectiveInference",  "stabs", "hal9001",
                   "igraph",
                   "randomForestSRC", "riskRegression", "rms", "party", "ggplot2", "bootstrap", 
                   "mice", "smcfcs"))
tinytex::install_tinytex()                   
```
and then it is necessary to install the following packages
```
# Install libraries from BioConductor
BiocManager::install("graph")
BiocManager::install("RBGL")
BiocManager::install("Rgraphviz")
install.packages(c("micd", "pcalg"))
remotes::install_github("annennenne/causalDisco")
```

2025