PhD Course Basic Statistics for health researchers
Table of Contents
Welcome
This is the course homepage of the PhD course "Basic statistics for health researchers (6 weeks)". The course description, including the learning objectives, is available from the website of the PhD school, here:
The webpage will be updated regularly during the course. Check the footnote at the bottom of the page for the date and time of the last update.
Place & Schedule
The course takes place at the Center for Sundhed og Samfund - CSS - Øster Farimagsgade 5, 1014 Copenhagen. Classes run from 8:00 to 15:00. The room numbers and schedule are given in the following table.
| Date | Day | Room (8:00-15:00) | Topics | Teachers |
|---|---|---|---|---|
| 16 February 2026 | Monday | CSS-7.0.22 | Overview, data, descriptive statistics, concept of statistical inference, confidence intervals | Paul Blanche, Gabriele Pittarello |
| 18 February 2026 | Wednesday | CSS-2.0.22 | Hypothesis testing, tests for continuous outcomes, multiple testing | Paul Blanche, Gabriele Pittarello |
| 23 February 2026 | Monday | CSS-7.0.22 | Univariate linear regression, correlation, regression to the mean | Paul Blanche, Gabriele Pittarello |
| 25 February 2026 | Wednesday | CSS-7.0.22 | Analysis of Variance (One-way and Two-way ANOVA) | Paul Blanche, Gabriele Pittarello |
| 2 March 2026 | Monday | CSS-7.0.22 | 2x2 tables, odds ratio, two sample tests for binary responses | Karl Bang Christensen, Gabriele Pittarello |
| 4 March 2026 | Wednesday | CSS-7.0.22 | Logistic regression | Paul Blanche, Gabriele Pittarello |
| 9 March 2026 | Monday | CSS-7.0.22 | Multiple linear regression, confounding, interaction | Paul Blanche, Gabriele Pittarello |
| 11 March 2026 | Wednesday | CSS-7.0.22 | Repeated measurements | Paul Blanche, Gabriele Pittarello |
| 16 March 2026 | Monday | CSS-7.0.22 | Survival analysis | Paul Blanche, Gabriele Pittarello |
| 18 March 2026 | Wednesday | CSS-7.0.22 | Optional: feedback/advice and work on homework assignment | Paul Blanche, Karl Bang Christensen |
| 25 March 2026 | Wednesday | CSS-7.0.22, CSS-7.0.06 | Presentation and discussion of homework assignments | Paul Blanche, Karl Bang Christensen |
Lectures and R-demo
Lecture notes and accompanying R-demos should be available via the links in the table below no later than two days before the course day. Some R-demos use external data not already available in R packages. Those are also provided via the table below.
| Course Day | Handout (1x1) | Handout (2x2) | R-demo | External Data |
|---|---|---|---|---|
| 1 | Lecture-1 | Lecture-1-2x2 | Rdemo-1 | none |
The course is not based on a single textbook, but the following book is an excellent reference. It covers most of the topics of the course (often in more details) and has been written for a similar audience to that of that course. Regression with linear predictors, by Per Kragh Andersen and Lene Theil Skovgaard (Springer, 2010). You have access to the pdf of the book via the Royal Danish Library (KU login needed).
Additional reading and video material
To complement the lectures and practicals, for each course day we recommend short videos and papers, to watch/read preferably before or after each course day. This material is independent of the lectures and practicals. It has been developed by teachers which are not involved in that course.
We recommend this material because we find it both entertaining and educative.
When you watch/read before the course day as recommended, it should help you to better follow the lecture. When you watch/read after the course day, it should help you to revisit some important concepts and/or further learn on a few selected topics.
| Course Day | What to read/watch ? | When to read/watch? |
|---|---|---|
| 1 | StatQuest: The Normal Distribution, Clearly Explained!!! (5 mins) | Preferably Before |
| Statistics Notes: Standard deviations and standard errors (10 mins) | Preferably Before |
Practicals
Practical experience with data analysis and statistical methods will be learned with an emphasis on understanding of the output of the statistical software and the interpretation of the results. Solutions are provided only for the statistical software R. They are made available at the end of each class.
| Course Day | Exercise | External Data | R code solution | Full Solution (R code & answers) |
|---|---|---|---|---|
| 1 | Exercise-1 | SCD | Not available yet | Not available yet |
How to prepare for the course
Internet
You will work with your own laptop. You will need Internet during the course. It is your own responsibility to be able to connect, but you should be able to use the wireless net of the university, eduroam (login needed).
Statistical software
The focus of this course is not on how to use statistical software. But statistical software is needed for all data analyses and examples that illustrate the statistical methods. The free statistical software R will be used throughout the course, via R-studio (https://posit.co/downloads/).
A minimum level of familiarity with basic R is considered as a prerequisite. This minimum level corresponds to that obtained after completing the course “Introduction to R for basic statistics” (taking place two weeks before this course). This might be obtained via the online introduction at https://biostat.ku.dk/r/ , although it is often best achieved by attending the in-person course. The estimated number of hours to complete the online introduction is 10 to 15 hours, depending on your R- and technical skills.
The participants are expected to use their own laptops during the course, to have installed all relevant software and to have downloaded all data for use during the course.
R packages
To run the code of the R-demos and to solve the exercises with R, specific packages need to be installed. Below is the list of packages needed for each course day, in addition to those needed for the previous days. Please install the packages before the start of each course day. The list will be updated no later than two days before the course day.
Some of the packages are essential to use specific functions, others are only used to load data examples.
| Course Day | R packages |
|---|---|
| 1 | DoseFinding , MESS , timereg , HistData |
How to pass the course
To pass the course you need to
- attend 80% of all teaching units (we count the signatures; optional day on 18/3 does not count).
- turn in your homework assignment in due time.
- present the results of your homework during the last course day.
Homework assignment
A homework assignment will be handed out in due time. Participants work with their own data or data related to their own research provided by their PhD supervisor. The homework assignment is turned in during the second last week of the course (deadline is Friday March 20). Before the deadline you can attend one day (non mandatory, on March 18), during which you can work on the assignment and receive feedback from the teachers.
More details will be provided later.