Medical Risk Prediction Models: With Ties to Machine Learning is a hands-on book is for clinicians, epidemiologists, and professional statisticians who need to make or evaluate a statistical prediction model based on data. The subject of the book is the patient's individualized probability of a medical event within a given time horizon. Gerds & Kattan describe the mathematical details of making and evaluating a statistical prediction model in a highly pedagogical manner while avoiding mathematical notation. Read this book when you are in doubt about whether a Cox regression model predicts better than a random survival forest. FEATURES:- All you need to know to correctly make an Online risk calculator from scratch- Discrimination, calibration, predictive performance with censored data and competing risks- R-code and illustrative examples - Interpretation of prediction performance via benchmarks- Comparison and combination of rival modeling strategies via cross-validation