The purpose of a prediction model is to estimate the probability of the presence of a particular outcome as accurately as possible. Prediction models are often developed with clinical practice in mind, and involve combining information about individual patients to calculate an individual’s probability of illness or recovery. The model can then be presented in the form of a clinical predictive rule. General applicability – i.e. the accuracy of the prediction model when applied to new patients in the future – is another very important aspect.
Clinical Prediction Models and Machine Learning is a 4-days course. he course consists of an intensive programme of partly interactive lectures, combined with computer-based practical work. Examples taken from clinical practice will be used for the computer-based work.
If a course is [Full], you can still register, but you will be placed on a waiting list. We will contact you as soon as a place becomes available. At that time you can still decide whether you want to participate in the course.
Nowadays, access to data is becoming easier and easier and therefore the data sets are getting bigger and bigger. The problem when developing prediction models in these data sets include the difficulty of selecting the most important predictors from a large number of variables. If this is not done carefully, the quality of the prediction model can be adversely affected. Machine learning methods can be used to develop prediction models in these large data sets. Also, prediction models may be adjusted before they can be applied to new persons. All these issues are frequently overlooked or underestimated by clinicians and researchers.
The aim of the course is to provide better knowledge and understanding of the development of prediction models in smaller and larger data sets that are relevant to real-life practice. We will focus on common methods for selecting variables as backward selection but also more advanced Machine learning procedures as lasso regression and tree based methods as well as their pros and cons. Once prediction models have been developed, it is important to assess the quality of the prediction model. For example, we will look at whether the predictions of the model are accurate and will consider various ways of measuring performance by using measures for overall quality, discrimination and calibration. The question of applying the model to new (future) patients will also be addressed. An important element of this is investigating whether the performance of the prediction model deteriorates when it is applied to new patients. This component is entitled the validation of the prediction model and we will cover various techniques for internal and external validation of prediction models and ways to train and test the model by using bootstrapping and cross-validation.
The course consists of an intensive programme of partly interactive lectures, combined with computer-based practical work. Examples taken from clinical practice will be used for the computer-based work.
Day 1
The development and quality of prediction models, including:
Day 2
Introduction to the validation of prediction models
Day 3
Updating of prediction models
Day 4
Developing prediction model with many variables
The course is designed for PhD-students, practitioners and applied researchers working in the field of epidemiology, medicine, public health, psychology, human movement sciences.
The course is intended for anyone who wants to know more about prediction models, for example because they want to be able to better assess a research proposal or article, or because they are developing or wanting to make a prediction model themselves. It is also important to be able to make a proper assessment of the value of a prediction model for practice.
The following concepts are assumed known by participants at the start of this course:
– Knowledge of basic statistical tests as t-tests and regression analyses.
– Knowledge of some basic SPSS commands. Knowledge of R(Studio) is not a prerequisite.
The course materials (lectures, assignments, feedback of the assignments etc) are available on Canvas, our digital learning environment. The documents will remain available on Canvas for at least one year.
In order to do the computer practicals, you will need:
1. R and R studio. R and R studio can be downloaded for free at https://cran.r-project.org/.
and
2. SPSS. If you do not have SPSS installed yet, you can purchase it on Surfspot. Another option is to use IBM’s trial version: SPSS Software | IBM
Literature will be provided during the course.
Students participating in the course as part of the Master’s programme Epidemiology need to pass the exam in order to complete the course.
Students not participating in Master’s programme Epidemiology who sign up for this course as a separate / single course can optionally register for the exam. The examination fee is € 160 per registration.
You can register for the exam via the website: Exams. Registration will close 3 weeks prior to the exam.
Please note that you need to pass the exam in order to receive credits (EC).
A certificate of participation will be granted to all students who have attended at least 80% of the classes. The number of contact hours are stated on the certificate.
Only for Dutch medical specialists!
If you wish to be considered for accreditation points by the KNMG (PE-points), you must sign the attendance list on the final day of the course.
To qualify for these points, there is an attendance requirement of 100%.
Epidemiology and Data Science, Amsterdam UMC
