|Teaching method||Contact hours|
|Course coordinator(s)||dr. ir. AM Berendsen|
|Lecturer(s)||prof. dr. ir. P van 't Veer|
|prof. dr. ir. EJM Feskens|
|dr. FJB van Duijnhoven|
|Examiner(s)||dr. ir. AM Berendsen|
Language of instruction:
Assumed knowledge on:
Before you start this course you should be able to:
- understand and calculate effect measures (e.g., IP, IR, IPR, IRR, OR, PAR);
- interpret regression coefficients from multiple linear regression and simple logistic regression;
- perform basic statistics in R by writing R script (in R studio).
These learning outcomes are related to the course HNH-28303 Introduction to Analytical Epidemiology and Public Health, HNH-30403 Integration of evidence I, MAT-25303 Advanced Statistics (online) and HNH-32903 Intermediate Analytical Epidemiology: Confounding and Effect Measure Modification. In the introduction part of this course, some materials will be provided to rehearse the most important topics needed for this course.
In this course, you will focus on data analysis of case control studies and longitudinal studies using logistic regression, Cox proportional hazard models and mixed models. The focus is on adjustment of confounding and identification of effect measure modification. Knowledge about how to obtain a valid answer to a research question is a prerequisite for everybody conducting observational research. Confounding and effect measure modification are topics that should be dealt with during data analysis. The data analysis of case control studies and several types of longitudinal studies are prerequisites for a successful conduct of a master thesis and an essential skill for epidemiologists.
- This online course largely overlaps with HNH-31506 Analytical Epidemiology I: Modelling in Nutrition & Disease Research
- For this course you need to have basic knowledge on the statistical data analysis software R (Studio).
After successful completion of this course students are expected to be able to:
- execute and interpret data analysis using linear regression models, logistic regression models, general and generalized linear models, mixed models, and Cox proportional hazard models;
- execute and interpret stratified analysis;
- adjust for confounding and identify effect modifiers using both stratified analysis and statistical modelling;
- systematically organize and document data analysis, using the statistical program R (Studio)
Knowledge clips, e-module, literature study, individual assignments.
Remote proctored written exam consisting of a theoretical exam and a practical exam.
• Kleinbaum, D.G. (2012) Introduction to Survival Analysis, chapter 1.
• Dos Santos Silva, I. (1999), Cancer epidemiology: Principles and Methods, chapter 12.
• Hedeker, D. (2006) Longitudinal data analysis, chapter 1.
|Compulsory for:||MNHDL||Nutritional Epidemiology and Public Health||MSc||2DL|