PhD Course Basic Statistics for health researchers

Table of Contents


This is the course homepage of the English version of the PhD course "Basic statistics for health researchers". The course description, including the learning objectives, is available from the website of the PhD school, here:

Course Description

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
15 April 2024 Monday CSS-7.0.28 Overview, data, descriptive statistics, concept of statistical inference, confidence intervals Paul Blanche, Caroline Long
17 April 2024 Wednesday CSS-7.0.28 Hypothesis testing, tests for continuous outcomes, multiple testing Paul Blanche, Caroline Long
22 April 2024 Monday CSS-7.0.28 Univariate linear regression, correlation, regression to the mean Paul Blanche, Caroline Long
24 April 2024 Wednesday CSS-7.0.28 Analysis of Variance (One-way and Two-way ANOVA) Paul Blanche, Caroline Long
1 May 2024 Wednesday CSS-7.0.28 2x2 tables, odds ratio, two sample tests for binary responses Alessandra Meddis, Christoffer Sejling
6 May 2024 Monday CSS-7.0.28 Logistic regression Paul Blanche, Caroline Long
8 May 2024 Wednesday CSS-7.0.28 Multiple linear regression, confounding, interaction Paul Blanche, Christoffer Sejling
13 May 2024 Monday CSS-7.0.28 Repeated measurements Brice Ozenne, Christoffer Sejling
15 May 2024 Wednesday CSS-7.0.28 Survival analysis Paul Blanche, Christoffer Sejling
22 May 2024 Wednesday CSS-7.0.22 , CSS-1.1.17 Presentation and discussion of homework assignments Paul Blanche, Brice Ozenne

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
2 Lecture-2 Lecture-2-2x2 Rdemo-2 none
3 Lecture-3 Lecture-3-2x2 Rdemo-3 th , ckd

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).

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
2 Statistics Notes: Absence of evidence is not evidence of absence (10 mins) Preferably Before
  StatQuest: p-hacking and power calculations (19 mins) Before or After
3 Statistics Notes: Logarithms (10 mins) Preferably Before
  StatQuest: R-squared, Clearly Explained!!! (11 mins) Preferably After


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 for the exercises are provided only for the statistical software R.

Course Day Exercise External Data R code solution Full Solution (R code & answers)
1 Exercise-1 SCD Solution1.R Solution1.pdf
2 Exercise-2 none Solution2.R Solution2.pdf
3 Exercise-3 SCD Not available yet Not available yet

How to prepare for the course


You will work with your own laptop. You will need Internet during the course. It is your own responsibility to be able to connect.

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 (

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” (held just before this course) or the online course introduction at 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
2 nlme , coin
3 none

How to pass the course

To pass the course you need to

  • attend 80% of all teaching units (we count the signatures).
  • turn in your homework assignment in due time.
  • present the results of your homework during the last course day.

Homework assignment

A homework assignment is handed out after lecture 5. Participants work with their own data or data related to their own research provided by their PhD supervisor. The homework assignment is turned in after lecture 9.

Created: 2024-04-17 Wed 21:13