Repeated Measurements [EL002]

Course highlights

EC points

1.7

Start date

03-04-2023

End date

14-04-2023

Course days

Online modules, class days: Mon 03/04, Wed 05/04, Thu 06/04, Tue 11/04, Thu 13/04, Fri 14/04,

Faculty

Prof. Dimitris Rizopoulos, PhD

Course fee

€ 910

Location

Hybrid

Level

Advanced

Prerequisites

Biostatistics I (CK020, previously CC02) and Biostatistics II (CK030, previously EP03), or equivalent knowledge (i.e., familiarity with standard regression models such as the multiple linear regression and logistic regression model, and R). No previous experience of repeated measurements analysis is required.

Disciplines

  • Biostatistics

Application

How to apply

Detailed information about this course:

Description

This course covers statistical methods to be used when one or more variables are repeatedly measured in time on the same experimental unit. For instance, in a clinical trial, the outcome variable can be measured at baseline and at different times during the treatment period. In a meta-analysis, the study can be regarded as the experimental unit and the observations of patients within the same study as repeated measurements.

In the last 10 or 15 years much progress has been made in the development of new methods of analysis. In recent years several of these new methods have been implemented in a wide variety of computer packages.

The course starts with a short overview of simple methods for analyzing repeated measurements data, followed by a short recap of the most basic concepts of linear algebra needed for the presentation of the most advanced models.

Then the main focus turns on more advanced methods. For approximately normally distributed repeated measurements outcomes marginal and linear mixed models are introduced. For non-normal responses, first the generalized estimating equations (GEE) approach for marginal inferences is presented, followed by extensions of random effects models to categorical outcomes. All these methods are exemplified using data from of clinical and epidemiological studies.

Computer practicals in the statistical programming language R will be used to acquire hands on experience in applying these techniques to real data. All code used during the course will be live demonstrated using a web app, which will be made avaliable to participants.


Participants need to bring their own laptop with certain pre-installed software (instructions will be given before start of the course). The assignment for this course needs to be handed in two weeks after the ending of the course.

Please note that this courses has a blended design, which means that it includes both online modules and live meetings. The first meeting lasts 1 hour, the following meetings last 2 hours. Class days are listed under 'Course days' above.

Disclaimer: This course is part of a trial with hybrid courses (can be followed both at Erasmus MC and online). In the event the hybrid setup is no longer possible, the course will be taught fully online in order to ensure all participants are able to join the course. However, we fully intend to organize this course as a hybrid course. Participants will be asked at a later stage to indicate whether they want to participate in class or online - note that there are limited in-class places available.

Objectives

Ability to use statistical methods where one or more variables are repeatedly measured in time on the same experimental unit.

Participant profile

For everybody using repeated measurements in their (clinical) research.

Assessment

Assignment(s)


Reduction on fees

PLEASE NOTE THIS DOES NOT APPLY TO THE RESEARCH MASTERS (120 EC points)

No fees are charged for  Erasmus MC PhD candidates, provided they have an account in Hora Finita, the Erasmus University PhD registration system.

Please note that in case of cancellation or no show, the cancellation policy applies based on the full course fee.

 

25% reduction for all (international) PhD candidates without formal appointment at Erasmus MC

Upon receipt of your application you will receive a request to upload proof of enrollment as a PhD candidate.