Medical Risk Prediction Models: With Ties to Machine Learning (Chapman & Hall/CRC Biostatistics Series)

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

• | Author: Thomas A. Gerds
• | Publisher: Chapman and Hall/CRC
• | Publication Date: February 01, 2021
• | Number of Pages: 312 pages
• | Language: English
• | Binding: Hardcover
• | ISBN-10: 113838447X
• | ISBN-13: 9781138384477