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.
Prognostic Research is one of the online medical courses of the MSc Epidemiology Postgraduate Online; the online MSc program in Epidemiology offered by Utrecht University, University Medical Center Utrecht, MSc Epidemiology and Elevate Health.
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
- Make 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.
- The principles of prognostic research and the interpretation of its results
- The issue of missing values and data analysis in prognostic research, including the underlying assumptions and their statistical consequences, and the evaluation of model performance
- The practical application of prognostic research and its clinical consequences
- Modelling techniques for small data sets
The whole course will take place online. The following learning methods will be used:
- Web lectures
- Reading articles
- Online discussion forums
- Individual and group assignments
Please note that you are required to hand in assignments during some of the learning units in this course:
Sunday before start date - introduce yourself
Sunday – complete Learning Unit 1 and 2
Sunday – complete Learning Unit 3 and 4
Sunday – complete Learning Unit 5 and 6
Monday – submit take-home exam
This Prognostic Research course includes a take home exam. No proctor is required for this exam and it is not compulsory. However, if you want to receive the Course Certificate and the credits, it is obligatory to take the exam. You are allowed to redo the exam once.
As this is a university course, it is covered by academic accreditation. Upon successful completion of this course, you will receive a recognized certificate from Utrecht University and the UMC Utrecht.
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 program R. Download R for free.
- 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. Elevate courses offering this knowledge include: Introduction to Epidemiology, Study Design, Introduction to Statistics, Classical Methods in Data Analysis, and Modern Methods in Data Analysis. You need to have successfully completed all of these courses, and preferably also Clinical Epidemiology, before enrolling for this Prognostic Research course.
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.
The entire course is also available offline. For more information about this option, please visit the MSc Epidemiology website.