Faculty of Health, Medicine and Life Sciences

Module Information
Good morning
EPI4923  - Advanced Statistical Analysis Techniques

Period 2: from 30-10-2023 to 22-12-2023
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: 13-10-2023

Deadline publication final result
The date on which the final grade of this module is available: 23-1-2024

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.

Resit date: 16-2-2024

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

Description: EN:

The major objective of this course is to prepare students optimally for the use of statistics in their practical work and the period after. Students are taught to apply the most commonly used statistical analysis techniques in a responsible way. Also, they should be better able to judge the statistical facets of research as carried out by others.

The training aims at applying advanced statistical techniques in a responsible way. The emphasis will be on concepts underlying the statistical techniques and on interpreting the results, with mathematics being kept to a minimum. The course material is primarily based on SPSS software. The use of R will only be briefly approached.

The following techniques will be treated

  1. Analysis of variance and covariance
  2. Linear regression 
  3. Logistic regression     
  4. Survival analysis  
  5. Analysis of repeated measures (linear multilevel models)

For each topic, there are two lectures and two tutorials. During the first tutorial, theoretical issues are discussed, while the emphasis on the interpretation of results obtained with SPSS on real data sets is given in the second tutorial. Concerning the lectures, the first one is more theoretical and involves the presentation of the method and the underlying assumptions. In particular, the consequences of violating the assumptions are investigated. The practical interpretation of software outputs is also of great interest. In the second part, we analyze a real dataset together and debate over the best choices to make to analyze the data. Then, we discuss how the results can be summarized to be presented to an audience with minimal statistical knowledge.

Goals: 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:

  1. Analysis of variance and covariance    
  2. Linear regression   
  3. Logistic regression     
  4. Survival analysis     
  5. Analysis of repeated measures (linear multilevel models)

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.  

Key words: EN:
Analysis of (co)variance, Linear regression, Logistic regression, Survival analysis, Analysis of repeated measures, Linear mixed models
Literature: This is the link to Keylinks, our online reference list.  
Teaching methods:
  • Assignment(s)
  • Lecture(s)
  • Problem Based Learning
  • Training(s)
Assessments methods:
  • Written exam

This page was last modified on:8-4-2024
No rights can be derived from data in this information system.       © 2024 J. van Emmerik