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Doctoral courses

Doctoral courses at MEB are presented here - some have dates set for the next time, but not all. We will update the course dates as soon as they are set.

Doctoral courses within the Epi-program given at the department

Fall 2019

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.

Biostatistics III: Survival analysis for epidemiologists (using R)
The course aims to introduce statistical concepts and methods for analysing time-to-event data with emphasis on applications in epidemiology and public health.

Multivariate prediction modelling 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.

Other courses within the Epi-program

An introduction to genetic and molecular epidemiology
The course focuses on basic concepts, methods and study design in genetic and molecular epidemiology research.

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.

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.

Causal inference for epidemiological research
Causal inference from observational data is a key task of epidemiology and of allied sciences such as sociology, education, behavioral sciences, demography, economics, health services research, etc.

Genetic Epidemiology
The course is about concepts and methods used in studies of genetic variation influencing disease and other phenotypes.

Infectious disease epidemiology
The course aims to show how epidemiological tools apply to the study of infectious diseases.

Introduction to R
To introduce students to using the R statistical software to perform basic to intermediate statistical data analysis in a replicable manner.

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 epidemiological data.

Reproductive Epidemiology
Fundamentals of human reproduction, overview of reproductive physiology, definitions and epidemiology of reproductive terms.