|Course coordinator(s)||dr. ir. AM Berendsen|
|Lecturer(s)||prof. dr. HC Boshuizen|
|dr. FJB van Duijnhoven|
|dr. ir. AM Berendsen|
|prof. dr. ir. E Kampman|
|prof. dr. ir. P van 't Veer|
|prof. dr. ir. EJM Feskens|
|Examiner(s)||dr. FJB van Duijnhoven|
Language of instruction:
Assumed knowledge on:
Before your start this course, be sure you are 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);
- 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);
The above-mentioned learning outcomes are related to the courses “MAT-15303 Statistics 1” plus “MAT-15403 Statistics 2” or “MAT-14303 Basic Statistics”, “MAT-24306 Advanced Statistics for Nutritionists”, “HNH-24806 Introduction to Epidemiology and Public Health”, “HNH-31006 Study Design and Interpretation in Epidemiology and Public Health”.
- HNH-31606 Analytical Epidemiology II – Causal thinking and analysis;
- Thesis Nutrition and Disease;
- Thesis Global Nutrition.
In this course, you will focus on data analysis and interpretation of the results of cross-sectional studies, case control studies and longitudinal studies in humans for studying diet-disease associations.
The focus is on adjustment of confounding and identification of effect measure modification.
This course can not be combined in an individual study programme together with HNH-37306 Applied data analysis or HNH-30806 Analytical Epidemiology, when in doubt, contact your study adviser. This course can not be combined in an individual programme with HNH-33403 Advanced Analytical Epidemiology (online) and/or HNH-35903 Integration of evidence II (online).
After successful completion of this course students are expected to be able to:
- execute and interpret descriptive statistics as a way to explore a dataset prior to studying diet-disease associations;
- explain stratified analysis, linear regression analysis, logistic regression analysis, survival analysis and longitudinal data analysis while handling confounding and effect measure modification in studying diet-disease associations;
- perform stratified analysis, linear regression analysis, logistic regression analysis, survival analysis and longitudinal data analysis while handling confounding and effect measure modification and interpret the data - following from these analysis in studying diet-disease associations;
- perform methods to adjust for energy, including the multivariate, density, and residual approach in a statistical program and interpret the coefficients of each of the three energy adjustment approaches;
- organize and document data analysis systematically for studying diet-disease associations while using the statistical program R;
- design a protocol for data analysis by identifying potential confounding variables and effect measure modifying variables based on literature and review a data analysis plan of others;
- explain the difference between the epidemiological and statistical approach while studying confounding and effect measure modifiers.
- E-modules and exercises on the computer;
- Group work;
All course material is available in the learning environment of this course.
The course material includes lectures, knowledge clips, readings from several books and papers, exercises, and assignments.
Course material will include among others:
- Statistical software: R (Studio), available on WUR computers;
- Petrie & Sabin, 'Medical Statistics at a Glance’, selected chapters;
- Webb & Bain, ‘Essential Epidemiology an introduction for students and health professionals, 3rd edition, selected chapters;
- Rothman, ‘Modern Epidemiology’, selected pages;
- Kleinbaum D.G., Klein M. (2012), Chapter 1: Introduction to Survival Analysis. In: Survival Analysis. Statistics for Biology and Health. Springer, New York, NY;
- Dos Santos I., (1999), Chapter 12: Introduction to survival analysis. In: Cancer Epidemiology: Principles and methods. International Agency for Research on Cancer, France;
- Hedeker, D (2006), ‘Longitudinal data analysis', chapter 1: Introduction.
|Verplicht voor:||MNH||Nutrition and Health||MSc||A: Spec. A - Nutritional and Public Health Epidemiology||4WD|
|Keuze voor:||MNH||Nutrition and Health||MSc||E: Spec. E - Systems Approach for Sustainable and Healthy Diets||4WD|