Doctoral courses
Doctoral courses at MEB are presented here - some have dates set for the next time, but not all. We update the course dates as soon as they are set.
Doctoral courses within the Epi-program given at the department.
Fall 2023
An introduction to genetic and molecular epidemiology
The course focuses on basic concepts, methods and study design in genetic and molecular epidemiology research.
Design and Analysis of Twin and Family-Based Studies
This course focuses on potential designs and analyses using twin- and family-data. Methods to estimate within-family associations and heritability are covered.
Biostatistics III: Survival analysis for epidemiologists *
This course focuses on the application of survival analysis methods to epidemiological studies.
Multivariate Prediction Models, Machine Learning and AI with Applications in Precision Medicine
This course aims to provide an introduction to both supervised and unsupervised methodologies for prediction modelling with a focus on biomedical applications, molecular epidemiology and personalised medicine. The main objective of the course is to provide basic theory and to facilitate for the course participants to acquire practical knowledge that will enable to apply covered methodologies in their own research.
Causal Inference for Epidemiological Research
This course aims to present causal theory and introduces how concepts and methods can be understood within a general methodological framework.
Applied Longitudinal Data Analysis
The aim of the course is to introduce modern methods for the analysis of longitudinal and repeated measures studies which are commonly used in epidemiological studies and in clinical trials.
Spring 2023
Biostatistics II: Logistic regression for epidemiologists
This course focuses on the application of logistic regression in the analysis of epidemiological studies.
Biostatistics III: Survival analysis for epidemiologists
The course aims to introduce statistical concepts and methods for analysing time-to-event data with emphasis on applications in epidemiology and public health.
Introduction to R
To introduce students to using the R statistical software to perform basic to intermediate statistical data analysis in a replicable manner.
Clinical Cancer Genomics
To facilitate that students get an understanding of basic theory and obtain practical knowledge that will enable course participants to apply the covered methodologies in their own research or clinical laboratory.
Other courses within the Epi-program
Epidemiology I: Introduction to epidemiology
The aim of the course is to give an introduction to epidemiological theory and practice.
Extensions to the design and analysis of case-control studies
To enable practicing epidemiologists to make more efficient use of already-available case-control data and to design case-control studies that will extend the possibilities for future analysis.
Biostatistics I - Introduction for epidemiologists
The course aims to introduce statistical concepts and methods with emphasis on applications in epidemiology and public health.
Infectious disease epidemiology
The course aims to show how epidemiological tools apply to the study of infectious diseases (currently not planned)
Introduction to Stata for epidemiologists
The aim of the course is to provide a basic knowledge of Stata, a statistical software program, for the analysis of epidemiology