Advanced Statistical Topics in Health Research B

This is the 2024 website for the course Advanced Statistical Topics in Health Research B held by the University of Copenhagen. This website provides practical information about the course.

Overview

The course will start on Monday, June 3rd and end on Friday, June 7th. No teaching on June 5th.
The course will run every day from 8.15 to 15. See the programme for more information.
Please install the necessary software before the course start. See below.
We will follow 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 notes for more information.
Teachers for the course will be Anne Helby Petersen, Benoit Liquet-Weiland, and Claus Thorn Ekstrøm.

Additional information will be given on the first day.

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”.
Next step is installing stan for R. Install the rstan package for R as per these instructions.

Once rstan is installed we need the rethinking package. That package is not on CRAN so we will install it directly from github. You do that by running the following three lines:
```
install.packages(c("devtools","mvtnorm","loo","coda"), repos="https://cloud.r-project.org/",dependencies=TRUE)
library("devtools")
install_github("rmcelreath/rethinking")
```

Then you should install the following bunch of R packages:

install.packages(c("isdals", "rstan", "brms", "rstanarm", "bayesplot", "MESS", "pROC", "igraph", "BiocManager", "devtools", "pdftools", "dagitty"))

Next, run this code to install further libraries from BioConductor:

BiocManager::install("graph")
BiocManager::install("RBGL")
BiocManager::install("Rgraphviz")

Then run this code to install two remaining packages:

install.packages(c("micd", "pcalg"))
devtools::install_github("annennenne/causalDisco")

We will use a LaTeX backend for plotting. If you do not have LaTeX installed already, you may install it from R using the tinytex package:
```
install.packages("tinytex")
library(tinytex)
install_tinytex()
```
Now you should be ready to go! You can check the most crucial installation steps by testing if this code runs and produces a plot:
```
library(causalDisco)
data(tpcExample)
res <- tpc(tpcExample, order = c("child", "youth", "oldage"))
tplot(res)
```

2024