The programme is divided into four course days, combining theoretical sessions with practical exercises.
Day 1: The development and quality of prediction models, including:
- The characteristics of a prediction model
- The most frequently used methods for selecting variables
- The pros and cons of common methods for selecting variables
- Sample size recommendations to develop a prediction model
- Different measures of quality and how to interpret them (including explained variation, calibration, discrimination, ROC curve)
- Introduction to Spline regression models.
- Introduction R software
Day 2: Introduction to the validation of prediction models:
- The linear predictor (lp)
- Optimism and shrinkage
- Adjusting the intercept
- The internal and external validation of prediction models
- Train and test datasets (Bootstrapping and Cross-validation)
- Adjusting the slope
- External validation
- Generalizability of prediction model (Case-mix, different regression coefficients)
- Presentation formats of prediction models
Day 3: Updating of prediction models:
- Reasons for generalizability problems
- Updating the intercept and slope
- Comparing Prediction models
- Adding a new Updating of Prediction models variable
- Reclassification tables
- A prediction model for survival data
Day 4: Developing prediction model with many variables:
- Developing prediction model with many variables
- Cross-validation
- Lasso Regression
- Model stability analyses
- Tree based methods
- Random forest
