Medical Risk Prediction Models: With Ties to Machine Learning

By Michael W Kattan and Thomas A Gerds

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.

• Format: Paperback
• Language: English
• ISBN: 0367673738
• ISBN13: 9780367673734
• Release Date: August 2022
• Publisher: CRC Press
• Length: 312 Pages
• Weight: 0.97 lbs.
• Dimensions: 0.7" x 6.1" x 9.2"