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EPI4923
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Advanced Statistical Analysis Techniques
Period 2: from 26-10-2026 to 18-12-2026 (maandag 26 oktober 2026 tot vrijdag 18 december 2026)
Co-requisites:
None
Coordinator:
Innocenti, F.
ECTS credits:
6
Language of instruction:
English
Publication dates timetable/results in the Student Portal
Deadline publication timetable
The date on which the timetable of this module is available:
not applicable
Deadline publication final result
Resit booking
Exam booking for a test in current academic year (resit)
You will be booked automatically for the resit in one of our resit periods. You may check our calenders to find out which modules can be retaken and when: https://intranet.maastrichtuniversity.nl/nl/fhml-studenten/studieverloop/wanneer-wat
As of one week before the resit test takes place, you can check in Student Portal if you are booked correctly: Student Portal > My Courses > More actions. The test will also be visible in your time table.
Exam booking for a test from a previous academic year (exam only)
All students who have not passed the test for this module in a previous academic year, will be booked automatically for the test during the regular block period. You will be enrolled in the new course in Canvas but not scheduled for a tutorial group and other educational activities.
If you do not wish to participate in this test at the end of the regular block period please de-register via askFHML.
Though great care has been taken to assure the accuracy of the information on fhmlweb, the FHML cannot be held responsible for possible printing errors, incomplete information, or misinterpretations. Additionally, the FHML reserves the right to make changes to this information.
Course information
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Description:
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EN:
The primary goal of this course is to equip you with the skills necessary to effectively use statistics in both your professional work and beyond. You will learn to apply the most widely used statistical analysis techniques responsibly and critically assess the statistical aspects of research conducted by others. Throughout the course, the focus will be on understanding the core concepts behind statistical methods and interpreting results, while keeping the mathematics to a minimum. This course uses SPSS as the primary software, though R users are also welcome. Please note that the focus of this course is on statistical models rather than on the software used to implement them. The following statistical models will be covered: - Analysis of variance (ANOVA) and covariance (ANCOVA)
- Linear regression
- Logistic regression
- Analysis of survival times
- Linear mixed-effects models for repeated measures
For each topic, the course includes two lectures and two tutorials. The first lecture introduces the theoretical foundations of a statistical technique, while the second lecture demonstrates its practical application. Similarly, the first tutorial covers theoretical concepts, and the second tutorial is dedicated to interpreting results generated using SPSS on real datasets.
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Goals:
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EN:
After completing this unit, the participants should have acquired the knowledge and skills required for the independent use and critical assessment of various (multivariable) statistical analysis concepts, procedures and techniques which are prominent in epidemiological research: - Analysis of variance (ANOVA) and covariance (ANCOVA)
- Linear regression
- Logistic regression
- Analysis of survival times
- Linear mixed-effects models for repeated measures
For each of these statistical techniques, the participant should be able to deal with confounding, interaction and outliers, be aware of the assumptions underlying the use of the technique, know some advantages and disadvantages of the technique, interpret results and use dummy coding. The participant should also be able to choose an appropriate statistical analysis strategy, given a specific epidemiological research question and study design.
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Key words:
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EN:
Analysis of (co)variance, Linear regression, Logistic regression, Survival analysis, Analysis of repeated measures, Linear multilevel and marginal models
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Literature:
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This is the link to Keylinks, our online reference list.
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Teaching methods:
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- Assignment(s)
- Lecture(s)
- Problem Based Learning
- Training(s)
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Assessments methods:
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This page was last modified on:dinsdag 14 april 2026
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