Modern Methods in Data Analysis (Online) - (2024-II)

Course description

This course provides statistical methods to study the association between (multiple) determinants and the occurrence of an outcome event. The course starts with an introduction to likelihood theory, using simple examples and a minimum of mathematics. Next, the most important regression models used in medical research are introduced. Topics are: maximum-likelihood methods, multiple linear and logistic regression, model validation and regression diagnostics, Poisson regression, and analysis of `event-history´ data, including an extensive discussion of the Cox proportional hazards regression model. Also, the basic principles of resampling methods (bootstrapping and permutation tests) and of longitudinal data analysis are taught.

This course can best be followed in the first half of the PhD track. It helps participants to identify the correct analyses for their research, carry out these analyses and interpret and report the results.

Learning objectives

At the end of the course you can name the situations in which the techniques can be applied and the conditions that should be met to obtain reliable results using these techniques. You are also able to explain and interpret the results obtained with the techniques, and apply these results in practice (e.g. to answer a research question).

In particular, you will be able to:

• Explain the principles of the likelihood theory and maximum likelihood methods
• Explain the principles of the following statistical analysis techniques: Logistic regression analysis, Poisson regression analysis, Analysis of event history data, including the Cox proportional hazards regression model
• Explain model validation and regression diagnostics
• Describe the basic principles of longitudinal data analysis
• Apply the above-mentioned techniques using common statistical packages (e.g. SPSS or R)
• Name the situations in which these techniques can be applied and the conditions that should be met to obtain reliable results using these techniques
• Explain and interpret the results obtained with these techniques, and apply these results in practice (e.g. to answer a research question)

Course program

The online course is a 9 week part-time course with a study load of 14 hrs/w. Our online courses enable you to study anytime, from any location in the world, as long as you have a working Internet connection. The course materials are accessible 24/7 and our asynchronous learning approach means there are no specific times or moments at which you should be online. You choose when and where you want to watch web lectures and work on assignments. There are weekly deadlines that you should take into account. The e-moderator of the course will inform you about all the deadlines at the start of the course. Part of our learning approach is the interaction with peers. In order to experience maximum interaction, and get the most out of this course, we advise you to log on several times a week. The course ends with an exam.

Start: 2 September 2024

End: 1 November 2024

Course material

You need to have access to and working knowledge of the freeware statistical program R.

Group size

8 to 30 participants

Number of credits

4.5 EC

Course certificate

Participants will receive a certificate when they completed the practical exercises (self-study), attended at least 80% of the classes and passed the exam.

Course fee

We offer 10 places for free for PhD candidates registered with the Graduate School of Life Sciences via MyPhD. Be quick, these 10 places tend to be taken soon. 

Cancellation and No-show policy

This course is free for GSLS PhD candidates. However: free of charge does not mean free of responsibility. Once you have signed up for a course, we expect you to attend. For every late cancellation or no-show we have had to disappoint others who would have liked to attend. This is our policy:

• You may cancel free of charge up to 4 weeks before the start of the course. After this date you can only cancel if you have a GSLS PhD candidate to replace you in the course. Send the name and contact information of your replacement to pcc@uu.nl, at least 2 working days before the start of the course;

• We expect that you actively attend the full course. You have to complete the total E-learning.

• Not meeting the above requirements means you will be charged a no-show fee. We will send the invoice after the course has ended. We are unable to make any exceptions, unless you have a valid reason (i.e., illness or death in the family 1^st/2^nd degree or partner). Your supervisor has to send an e-mail to pcc@uu.nl indicating the reason.

More information and registration

To register for this course, please send an e-mail to msc-epidemiology@umcutrecht.nl, including the 4 points below. Your registration can only be processed when you include all this information.

• You are a PhD candidate of the GSLS;
• You are registered at the GSLS in MyPhD (this is required. Without MyPhD registration you can't participate);
• The PhD programme of the GSLS that you are part of and your affiliation;
• The course that you would like to attend (Modern Methods in data analysis starting 02-09-2024).

Interest list

Is this course fully booked? Or would you like to follow a course at a later moment? When you subscribe to the interest list external link of a course, you will receive an e-mail when a new edition opens for registration. The interest list is for GSLS PhD candidates only.