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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:
Advanced Statistical Analysis Techniques covers main statistical techniques used in epidemiological research. In epidemiology, many studies examine relationships between two variables, often interpreted as cause and effect. Analysis depends on measurement type: correlation or regression for quantitative variables, cross-tabulation for qualitative variables, and tests for differences in means or proportions for mixed types. Simple analyses may be insufficient due to confounding or interaction effects. Advanced methods, including regression, ANOVA, and survival analysis, enable analysis of multiple variables in relation to one outcome. Repeated measurements on the same subject require methods that account for their correlation. This can be done with linear mixed models or marginal models for multiple time points, while a single pre-post measurement can be analyzed using ANCOVA (adjusting for the baseline) or ANOVA on the change scores, depending on whether the study is experimental or observational.
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Goals:
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EN:
Knowledge and understanding - Understand and apply multivariable statistical methods in epidemiological research, focusing on interpretation rather than mathematical derivations.
- Understand ANOVA/ANCOVA, including confounding, interaction, assumptions, model hierarchy, and multiple testing corrections (Bonferroni, Tukey).
- Interpret output from statistical software (e.g., SPSS/R).
- Understand multiple linear regression, including dummy coding, confounding, interaction, collinearity (VIF/tolerance), and model diagnostics.
- Recognize repeated measures analysis methods, including marginal models and random effects models, and their assumptions and interpretation.
- Understand logistic regression for binary outcomes, including odds ratios, estimation methods, model building, interaction, and interpretation.
- Understand survival analysis methods, including Kaplan-Meier curves, log-rank tests, Cox regression, hazard ratios, censoring, and model selection (AIC/BIC).
Applying knowledge and understanding - Independently apply appropriate statistical methods in epidemiological research based on design, variables, and research questions.
- Develop statistical analysis plans as part of study protocols.
- Critically interpret statistical methods in scientific literature and conference presentations.
Making judgments - Critically evaluate statistical analyses in published epidemiological research.
Communication - Communicate statistical concepts and results to both expert and non-expert audiences.
Learning skills - Continuously expand statistical knowledge and skills toward more advanced and specialized methods through study or self-directed learning.
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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
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Literature:
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For those using SPSS: Field, A. Discovering statistics using IBM SPSS
statistics; 6th ed. London: Sage Publications Ltd, 2024. For those using R: Field
A. Miles J., Field Z. Discovering statistics using R; London: Sage Publications
Ltd, 2012. Link to literature
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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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- Attendance
- Participaion
- Written exam
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This page was last modified on:vrijdag 22 mei 2026
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