Aim and content

This is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH.

Anyone can apply for the course, but if you are not a PhD student at the Graduate School, you will be placed on the waiting list until enrollment deadline. After the enrolment deadline, available seats will be allocated to the waiting list.

The course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD students at NorDoc member faculties. All other participants must pay the course fee

Learning objectives
A student who has met the objectives of the course will be able to:
1. Analyze data using the methods presented and be able to draw valid conclusions based on the results obtained.
2. Understand the advantages/disadvantages of the methods presented and be able to discuss potential pitfalls from using these methods.

Content
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 based on ideas from Bayesian statistics. These methods are suitable for analyzing complex data and experimental designs encountered in health research such as supervised and non-supervised machine learning methods, principal component analysis and partial least squares, support-vector machines, network analysis, and causal learning.

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 good 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.

Note that there are two courses entitled “Advanced Statistical Topics in Health Research”. They have no overlap and can be taken independently of each other.

1. Introduction to Bayesian statistics and the difference between frequentist and Bayesian statistics.
– Credibility intervals, prior and posterior distributions
– Bayesian classifiers
– Markov-chain Monte Carlo (MCMC) estimation
– Empirical Bayes estimators

2. Network analysis
– Introduction to graphs and graph theory
– Visualizing graphs
– Identifying communities
– Latent variable models

3. Principal component analysis, partial least squares, and Support-vector machines
– Dimension reduction techniques
– PCA and PLS
– Sparse PCA and PLS
– Multiclass and non-linear SVMs

4. Causal Structure Learning
– Introduction to directed acyclic graphs (DAGs)
– Causal structure learning
– Algorithms and assumptions for causal learning

Participants
The course is tailored for Ph.D.-students in health sciences who already have taken the Ph.D.-course “Basic Statistics for Health Researchers” or have a similar knowledge about statistics, and who wish to have more knowledge about the statistical methods underlying the approaches presented in the course.

A basic knowledge of statistics and previous experience with the software program R is expected. However, little or no previous exposure to the topics covered is expected.

Relevance to graduate programmes
The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH:
– All graduate programmes

Language
English

Form
The course will consist of 4 full days with lectures before lunch and hands-on computer exercises after lunch each day.

Course director
Claus Thorn Ekstrøm, Professor, Section of Biostatistics, Department of Public Health, University of Copenhagen
ekstrom@sund.ku.dk

Teachers
Anne Helby Petersen, Assistant professor, Section of Biostatistics, Department of Public Health, University of Copenhagen
Benoit Liquet, Professor, School of Mathematics and Physics, University of Queensland, Australia
Claus Thorn Ekstrøm, Professor, Section of Biostatistics, Department of Public Health, University of Copenhagen
Others teachers from the section of Biostatistics, UCPH

Dates
Monday June 3rd 2024
Tuesday June 4th 2024
Thursday June 6th 2024
Friday June 7th 2024

All days 8.00-15.00

Course location
CSS

Registration
Please register before 25 April 2024

Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules.
Applications from other participants will be considered after the last day of enrolment.

Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.

Disclaimer:
DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.