This is the 2019 website for the course Advanced Statistical Topics in Health Research B held by the University of Copenhagen.

## Practical information

• The course will start on Monday, November 4th and end on Thursday, November 7th.
• Each day will take place at CSS (Kommunehospitalet), and we will be in room 2.1.12 (building 2, first floor, room 12) (see map at the end of this document). Final information about the room will be added Monday, November 28th.
• The course will generally run every day from 8.15 to 15 with a lunch break in between (see the programme for more information).
• Teachers for the course will be Claus Thorn Ekstrøm, Anne Helby Petersen, Thomas Gerds and Helene Rytgaard.

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:

• 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.

## Laptops and software

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

### Installation

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, 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",
"Matching", "ggplot2", "sandwich"))

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

Claus Ekstrøm 2019