Prognosis is a key concept in patient care. However, although prognostic research is becoming increasingly important in clinical medicine, the actual methodology behind it is relatively underdeveloped. The purpose of this course is to redress this imbalance. We will therefore discuss the principles and methods of non-experimental prognostic research, together with the practice of prognostic research in a clinical setting. The emphasis will be on learning about the design and statistical analysis of prognostic studies, the construction and estimation of prediction rules, the various approaches to validation, and the generalization of research results. You will also learn how to address the challenges of dealing with small data sets.
By the end of the course, you should be able to:
- Understand the key characteristics and different types of prognostic research
- Set out the various steps involved in performing prognostic research
In particular, you should be able to:
- Demonstrate an insight into different types of missing values
- Understand different ways of handling missing values in prognostic research
- Propose different modelling approaches for prognostic research, including non-linear models
- Develop a prognostic model
- Show how to derive a prognostic score, and choose adequate score cut-offs
- Know how to apply modelling techniques to deal with over-fitting in small data sets.
To enroll in this course, you need:
- A Bachelor's degree in life sciences (or the equivalent)
- Sufficient proficiency in English reading and writing
- Access to the free software environment R
- Basic knowledge of the statistical program R, which will be used in this course and in the final exam
- An intermediate level of understanding of statistical methods.
Please note this course is offered through the MSc Epidemiology program developed by the UMC Utrecht and Utrecht University. You do therefore need access to an Internet connection in order to be able to follow lectures, complete assignments and communicate with fellow participants.