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Predictive Analytics


Admission requirements

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


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;


Between semesters, we will transfer from using the scheduling app to using MyTimeTable: all educational activities before 6 February 2023 can be viewed on our LUMC scheduling website or in the scheduling app. View MyTimeTable for your schedule from 6 February onwards.

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: (20%) Peer review assessment

Week 3 – On campus: (30%) Group presentation

Week 4 – Final week: (50%) Take-home assignment
All components combined make up the 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 minimum result of 5,5 for the overall assessment is required to pass.
If the result is less than 5,5 or if the student did not participate in one of the components, the student is given the opportunity to resit the assessment as one assignment that covers all the learning goals of the course.

A final grade of 5,5 minimum is considered sufficient.

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 Leiden University Campus The Hague.