Validation of prediction models in epidemiology and medicine

Aalborg University, Doctoral School in Medicine, Biomedical Science and Technology

Clinical prediction models constitute a valuable resource in a complex health care organism with resource scarcity and can be applied in various context including capacity planning, decision support, diagnostics, screening, and exploratory analyses. Depending on the context these models are required to pass through several pre- and post-clinical implementation phases including pre-implementation validation, monitoring, and de-implementation.

The course covers the following topics:

1. The basic differences between explanatory and predictive studies.
2. The phases and contexts of prediction models for clinical and population medicine.
3. Variable selection, shrinkage and SHapley Additive exPlanations (SHAP).
4. N-fold cross validation, bootstrap, geographical and temporal validation.
5. Net-benefit, calibration, receiver operating characteristic curve, and Bland-Altman plots.
6. Decision curve analysis and model assessment with a clinical justified threshold probability.
7. Sample size estimation for development as well as for validation.
8. Examples of specific use cases from the scientific literature and live working applications.

Upon completion of the course, the student will be able to distinguish between predictive and explanatory data analyses, understand the different phases and contexts of model development, implementation, and validation, as well as understand the basic statistical tools used in predictive studies.

For additional information, updates, and registration, please refer to AAU PhDMoodle via the link provided on the right side of this page.

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