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Predictive Analytics


Admission requirements

Successful completion of Study Design and Responsible Data Analysis is advised.

Basic knowledge of biostatistical procedures. Specifically, knowledge about standard regression techniques in epidemiology: logistic regression and Cox regression.

If you are not enrolled in the MSc programme Population Health Management but you consider taking this course as an elective, please contact our study advisor.


Predictive analytics is an area of statistics, concerned with developing and evaluating tools for predicting a health outcome based on individual characteristics. This course aims to improve understanding of the role of predictive analytics for prevention, diagnosis, and intervention. We will discuss how to build, validate, and update prediction tools. We will also discuss the task of assessing their predictive performance in targeted settings. Key concepts and issues in prediction research will be covered, relating, for example, to adequate study design, sample size and statisytical overfitting. Case studies are used throughout for illustration. The core material of this course will be introduced through predominantly online lectures, which will be interspersed with quiz questions and exercises. The course also gives a hands-on experience in prediction research through R programming and example datasets.

Course objectives

Upon successful completion of this course, the student:

  • understands the roles that diagnostic and prognostic models may play
    in risk stratification, and ultimately medical decision-making;

  • knows the critical factors that determine the validity of predictions
    provided by diagnostic and prognostic models;

  • has insight in the pitfalls of model development with standard
    statistical techniques;

  • has both theoretical and practical knowledge on advanced methods
    in model development and validation, specifically on regression

  • understands the possibilities of using Electronic Health Records data
    for risk stratification;


All course and group schedules are published on MyTimeTable.

The exam dates have been determined by the Education Board and are published in MyTimeTable.
It will be announced in MyTimeTable and/or Brightspace when and how the post-exam feedback will be organized.

Mode of instruction

  • Lectures

  • Research

  • Online education

  • Group work

Assessment method

Students are assessed according to the following three obligatory components:

Week 1-2 – Online: Peer review assessment (20%, no required minimum grade)

Week 3 – On Campus: Group presentation (30%, no required minimum grade)

Week 4 – Final week: Final assignment (50%, required minimum grade: 6,0)

All components combined make up the final grade for the course. It is compulsory to participate in each of the components in order to receive a grade.
Details on the assessment can be found in the assessment plan on Brightspace.

A final grade of 6,0 minimum is considered sufficient. If the result is less than 6,0 or if the student did not participate in one of the components, the student is given the opportunity to retake the assessment as one assignment that covers all the learning goals of the course.

Final grades between 5,0 and 6,0 will be rounded:

  • 5,0-5,4 → 5,0

  • 5,5-6,0 → 6,0

Reading list

The reading list can be found on Brightspace. The material consists of presentations and pdf files. There is no need to purchase literature, as the presented material is not commercialized.
Extra in depth material is available in the book ‘Clinical prediction models’ by E.W. Steyerberg, published by Springer, for sale at Amazon,, Springer, etc.


Registration must be completed via MyStudyMap. Registration in MyStudyMap gives you automatic access to the course in Brightspace. For more information, please visit the Leiden University website for students.


Prof. dr. Ewout Steyerberg -


This course is a combination of online education and on campus education at the Leiden University Health Campus in The Hague.