Erasmus Summer Programme Courses
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Markers and Prediction Research [ESP62]
Monday to Friday (5 afternoons)
Prof. John Ioannidis, Dr. Maryam Kavousi
Erasmus MC, Rotterdam NL
Introductory level background in epidemiology (e.g. Principles of Research in Medicine and Epidemiology (ESP01) and biostatistics (e.g. Introduction to Data-analysis (ESP03)).
- Clinical Research
- Clinical Epidemiology
Online, download instructions will be sent in August by e-mail.
Further reading: Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating (Steyerberg, Springer Series: Statistics Statistics for Biology and Health, ISBN: 978-0-387-77243-1
Detailed information about this course:
Faculty: Prof. John Ioannidis, MD DSc & Maryam Kavousi, MD PhD
Prognostic research is of growing importance, as globally more people are living with disease and clinicians and policy makers seek ways of targeting existing treatments and improving health outcomes. There is a rapid expansion in the number of new prognostic markers. Often, bold claims are made about their potential to assist in personalising approaches to medical care and treatment. Prognostic models may be useful to summarize the effects of multiple predictors but while commonly developed, such models are often not well validated or used in clinical practice.
This course aims to provide the basic knowledge and principles to evaluate the quality of prognostic research and its translation to inform decision making of clinicians and policymakers. Drawing on recent examples and current controversies in cardiovascular disease, cancer, trauma and other conditions, the course examines molecular biomarkers and genetic variants through to the quality of healthcare as predictors of outcome. Topics include design, conduct and analysis of prognostic research; outcomes research; prognostic factors and prognostic markers; prognostic models for risk prediction; and stratified and personalised medicine.
There will be lectures, interactive debates and critical appraisal of papers, but no computer labs (the course does not cover advanced statistical methods, see Further reading).
The course is suitable for undergraduates medical students, practicing clinicians, and those contemplating or doing a Masters or PhD in a related area.