Intermediate Analytical Epidemiology: Confounding and Effect Measure Modification

The courses of Nutritional Epidemiology and Public Health are interesting for professionals who are conducting or using nutritional and/or health/disease research among various research target groups like patients, elderly or f.i. regional populations. It is also suitable for teachers in nutrition and/or health education, policy makers of (inter)national organisations or governments, managers of food or pharma industries that develop specific (medical) nutrition for target groups and the courses are furthermore open to anyone who wants to engage a career in the work field of nutritional epidemiology and public health. A solid background in statistics is needed and please pay attention to the specific prerequisite knowledge that is written below.

Prerequisite knowledge

Before you start this online master's course you 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 and Advanced Statistics (see related courses in the right-hand margin).

• 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

This online master's course focuses on applying appropriate epidemiological data analyses methods to study diet-disease associations in the general population or specific patient groups while handling confounding and effect measure modification.

Activities

This course is an online course at master level that you follow in a cohort. Learners participate at different time points and from different time zones, as most learners also have a job. The programme therefor offers learning activities that allow you to supervised self-study at your own pace, with deadlines for assignments, and can include knowledge clips, e-learning modules, online individual and group exercises and assignments, online discussions, and in some courses occasionally live question hours through MS Teams at specific dates and times. There are no online live classes. The exam has a fixed date.

This course is quite time-intensive and requires approximately 20 hours per week for the average participant. There are assignments with deadlines.

Software used in this course

R, including R studio.

Self-Paced Online Course Getting Started with R

You need to have a good basic understanding of statistics, and you need to have experience with software 'R'. If the latter is not the case, you can follow the Self-Paced Online Course Getting Started with R first. For more information and registration, please check the document linked in the right-hand column.

Literature

All course material will be available in the learning environment and include amongst others:

+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, E-book available through the WUR-library;
+Modern Epidemiology by Rothman, chapter 11, E-book available through the WUR-library.

Upon successful completion - passing the exam -, a digital Microcredentials certificate (EduBadge) with 3 study credits (ECTS) is issued. The EduBadge certifies the learning outcomes of short-term learning experiences, marking the quality of a course.

+an individual written assignment
+a remote proctored exam with closed and open questions
+a remote proctored exam with RStudio

Participation in the exam is optional. If you decide not to participate in the exam, you do not qualify for a certificate and/or micro-credential.