module 06: generalized linear models and model building

module 06: generalized linear models and model building

  • module 6: generalized linear models and model building
    09/09/2019 - 13/09/2019
    1:45 pm - 12:45 pm
    Mon 9/09: 1.45 pm - 5 pm | S9 pc room 3.1
    Tue 10/09: 1.45 pm - 5 pm | S9 pc room 3.1
    Wed 11/09: 1.45 pm - 5 pm | S9 pc room 3.1
    Thu 12/09: 1.45 pm - 5 pm | S9 pc room 3.1
    Fri 13/09: 11.15 am - 12.45 pm | S9 pc room 3.1

Course details

week 1


This course elaborates on generalized linear models by extending linear models with continuous outcomes to models with discrete outcomes (binary and count data). These are so called generalized linear models. In addition, different techniques on how to build a good model are discussed.

Course structure

The course contains 4 topics: (1) overview of generalized linear models, (2) logistic regression, (3) Poisson regression and (4) model building. Each topic consists of a combination of a theoretical session and practical hands-on exercises in R in which participants can try out the theory by practical examples. In the first topic, we repeat the characteristics of linear models, which are a subgroup of generalized linear models. We explain why generalized linear models are needed and go deeper into the characteristics of these models (e.g. exponential family, link function, variance function, Maximum Likelihood). Second, we explain 2 important generalized linear models in detail: logistic regression and Poisson regression. Link functions, estimation, inference and goodness-of-fit of these models are discussed and examples are given. In addition, we discuss variants of these models such as Exact, conditional and Firth’s for logistic regression and Zero-inflated and Hurdle models for Poisson regression. Finally, we talk about good model building procedures including forward, backward and stepwise model building. Methods to compare models are discussed (likelihood ratio tests, AIC) and we review the issues of interactions, confounding and multicollinearity.
You need to bring your own laptop with the latest versions of R and R studio installed.


  • Hypothesis testing
  • Linear regression
  • Simple linear regression
  • Multiple linear regression
  • Inference
  • Testing for continuous variable
  • Testing for categorical variable
  • Confidence intervals
  • Diagnostics
  • Analysis of Variance
  • Inference
  • Diagnostics
  • Basic knowledge of programming and fitting linear models in R


Dr. Emmanuel Abatih is a post-doctoral research fellow at Ghent University and he works as the FLAMES coordinator for UGent and as a statistical consultant for FIRE. He obtained a PhD in Life Sciences in 2008 at the University of Copenhagen on the topic: “Assessment of the impact of the non-human use of Antimicrobial Agents on the Selection, Transmission and Distribution of Antimicrobial Resistant Bacteria” . He worked for the Institute of Tropical Medicine (ITM) in Antwerp, as a post doc assistant on topics including: space-time analysis, diagnostic test elevation, transmission dynamic modeling and risk analysis. He served as a statistical consultant for the TB, Malaria and Parasitology units of the ITM. He has supervised/co-supervised over 30 masters and 7 PhD students. He has experience with R, python, SATSCAN, SAS and STATA.


Venue Phone: +32 9 264 52 39

Venue Website:

Krijgslaan 281, Gent, 9000, Belgium

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