HNH-32903 Intermediate Analytical Epidemiology: Confounding and Effect Measure Modification (online)

Code last year: (HNE-32903)


Credits 3.00

Teaching methodContact hours
Knowledge clip0
Individual Paper0
Independent study0
E-learning material0
Course coordinator(s)dr. ir. AM Berendsen
Lecturer(s)prof. dr. ir. E Kampman
prof. dr. ir. P van 't Veer
Examiner(s)dr. FJB van Duijnhoven

Language of instruction:


Assumed knowledge on:

Students should be able to:
- describe the characteristics of major study designs (i.e., cohort study, case-control study, cross-sectional study);
- understand and  calculate effect measures (e.g., IP, IR, IPR, IRR, OR, PAR);
- explain the concepts causality, validity, external validity, internal validity, selection bias, information bias, confounding, effect measure modification, stratification, precision, and bias;
- analyse an association between an exposure and a continuous outcome;
- interpret regression coefficients from linear regression;
- perform basic statistics in R by writing R script (in R studio).

These learning outcomes are related to the courses "Introduction to analytical epidemiology and public health", "Integration of evidence 1", "Advanced statistics for distance learning" and "Observational designs and assessment of validity".

Continuation courses:

HNH-33403 Advanced analytical epidemiology


Note: This course can not be combined in an individual programme with HNH-37306 Applied Data Analysis.
This course focuses on how to identify confounding and effect measure modification and how to deal with confounding and effect measure modification in the data analysis. The main focus will be on confounding.

Learning outcomes:

After successful completion of this course students are expected to be able to:

- identify confounding and effect measure modification by stratification;

- adjust the association for confounding by calculating a pooled estimate;

- identify confounding and effect measure modification by linear regression modelling;

- apply linear regression modelling to adjust an association for confounding and to increase precision;

- understand and appreciate the difference between the epidemiological and statistical approach;

- identify potential confounding variables and effect measure modifying variables based on literature and include this in a data analysis plan;

- understand and apply logistic regression, and interpret the results;

- understand the principles of energy adjustment;

- understand the relevance of energy adjustment because of  correction for diet composition, removal of external variation in intake, or confounding;

- understand methods to adjust for energy, including the multivariate, density, and residual approach;

- apply the approaches for energy adjustment in R;

- interpret the coefficients of each of the three energy adjustment approaches.


Clips, e-module, individual assignment, group discussion.


- theoretical exam 60%; 
- practical exam 20%;
- individual assignment: data analysis plan 20%.


- Medical statistics at a glance by Petrie & Sabin, chapter 27, 28, 29 & 30;
- Essential Epidemiology, an introduction for students and health professionals by Webb & Bain, chapter 8;
- Modern Epidemiology by Rothman, chapter 11.

Compulsory for: MNHNutrition and HealthMScE: Nutritional Epidemiology and Public Health1DL