Medical Risk Prediction Models

The backbone of medical decision making is prediction. Statistical prediction models can help in medical decision making. This book takes the viewpoint of the single patient and asks what does it mean that a risk prediction model performs well for a single individual?

---

Medical Risk Prediction Models: With Ties to Machine Learning is a hands-on book 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 and 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, and 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.