Medical Risk Prediction Models: With Ties to Machine Learning

by Thomas A. Gerds, Michael W. Kattan

Overview

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.

Product Details

ISBN-13:	9780367673734
Publisher:	CRC Press
Publication date:	08/29/2022
Series:	Chapman & Hall/CRC Biostatistics Series
Pages:	312
Product dimensions:	6.12(w) x 9.19(h) x (d)

About the Author

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

Michael 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.

Table of Contents

1. Software. 2. I am going to make a prediction model. What do I need to know? 3. Regression model. 4. How should I prepare for modeling? 5. I am ready to build a prediction model. 7. Does my model predict accurately? 7. How do I decide between rival models? 8. Can't the computer just take care of all of this? 9. Things you might have expected in our book.