Advanced Analysis of Prognosis Studies [EL014]
Wednesday to Friday
Prof. Ewout Steyerberg, PhD, David van Klaveren, PhD
Erasmus MC, Rotterdam NL
Knowledge of basic epidemiological notions, especially cohort studies and randomized controlled trials.
Knowledge of statistical concepts (t-test, normal distribution, correlation) and regression techniques (linear, logistic, Cox regression).
- Clinical Epidemiology
Detailed information about this course:
Prognostic models are increasingly published in the medical literature each year. But are the results relevant for clinical practice? What are the critical elements of a well developed prognostic model? How can we assume that the model makes accurate predictions for our patients, and not only for the sample that was used to develop the model (generalizability, or external validity)?
In the course we will address these and other questions from a methodological perspective, using examples from the clinical literature.The participants will be encouraged to participate in interactive discussions and in practical computer exercises.
- Increasing the knowledge of the roles that prognostic models may play in clinical practice and the critical factors that determine the validity of predictions from a prognostic model.
- Gain insight in the pitfalls in prognostic model development with standard statistical techniques.
- Acquire both theoretical and practical knowledge on advanced statistical techniques in prognostic model development and validation, specifically on regression modeling.
Reduction on fees
PLEASE NOTE: This does not apply to the fee of the research master programmes (120 EC points)
No fees are charged for Erasmus MC PhD candidates, provided they have an account in Hora Finita, the Erasmus University PhD registration system.
In case of cancellation or no show, the cancellation policy applies based on the full course fee.
25% reduction for all (international) PhD candidates without formal appointment at Erasmus MC
Upon receipt of your application you will receive a request to upload proof of enrollment as a PhD candidate.