Generalized Linear Models

Course goals

ALL PARTICIPANTS WILL BE PLACED ON THE WAITING LIST UNTIL TWO WEEKS BEFORE THE START OF THE COURSE

GRADUATE STUDENTS:
Please be aware that you can only select a course option that shows the academic year and is offered Face-to-Face (F2F)

POSTGRADUATE STUDENTS:
Please be aware that you can only select a course option that shows the academic year and is offered Face-to-Face (F2F)  or online (depending on your registration)

(FYI: the other options are options for Continuing Education (onderwijs voor professionals))


At the end of the course, the student will:

know the role of link functions and error distributions
be familiar with the most commonly used generalized linear models
know when to apply which model in practice
know the most commonly used methods for checking model appropriateness and model fit
be able to perform GLM analyses using the appropriate software (R and SPSS)
be able to interpret the output and report the results of GLM analyses in terms of the context of the research question

Content

Contact details: Educational Office Epidemiology
E-mail: msc-epidemiology@umcutrecht.nl

Registration:
You can register for this course via Osiris Student. More information about the registration procedure can be found here on the Students' site. NOTE Students of the MSc Epidemiology (Post Graduate) that register in time (i.e. at least two weeks before the start of a course) will always be admitted to the course unless it is completely full. Other students will receive information about their application two weeks before the start of the course.

Course coordinator:
Dr. R.K. (Rebecca) Stellato, UMC Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht, the Netherlands

Course description:
The generalized linear model (GLM) is a flexible generalization of ordinary least squares regression. The GLM allows the linear model to be related to the response variable via a link function together with an error function. Starting with the familiar linear regression and ANOVA, the course will expand the linear model to include link functions such as the logit with binomial and the log with Poisson error distributions, thereby enabling students to model outcome variables that are not continuous. Attention will be paid to likelihood estimation methods and the checking of model assumptions.

Literature/study material used:
Faraway, JJ. Extending the linear model with R : Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition. Chapman and Hall/CRC , 2016. Note: textbook is recommended, but not required.
  
Mandatory for students in own Master’s programme:
MIght be for a specialization programme of Epidemiology & Epidemiology Postgraduate
 
Optional for students in other GSLS Master’s programme:
Yes
 
Prerequisite knowledge:
Introduction to Statistics
Classical Methods in Data Analysis
Modern Methods in Data Analysis