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

Thomas A.

Gerds is a professor at the Biostatistics Unit at the University of Copenhagen and is affiliated with the Danish Heart Foundation. He is the author of several R-packages on CRAN and has taught statistics courses to non-statisticians for many years.

Michael W.

Kattan is a highly cited author and Chair of the Department of Quantitative Health Sciences at Cleveland Clinic. He is a Fellow of the American Statistical Association and has received two awards from the Society for Medical Decision Making: the Eugene L. Saenger Award for Distinguished Service, and the John M Eisenberg Award for Practical Application of Medical Decision-Making Research.



Format Häftad Omfång 312 sidor Språk Engelska Förlag Taylor & Francis Ltd Utgivningsdatum 2022-08-29 ISBN 9780367673734