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

Gerds, Thomas A.; Kattan, Michael W.

ISBN 10: 113838447X / ISBN 13: 9781138384477

Published by Chapman and Hall/CRC, 2021

About this Item

Seller Inventory # I-9781138384477

Bibliographic Details

• Title: Medical Risk Prediction Models: With Ties to Machine Learning (Chapman & Hall/CRC Biostatistics Series)
• Publisher: Chapman and Hall/CRC
• Publication Date: 2021
• Language: English
• ISBN 10: 113838447X
• ISBN 13: 9781138384477
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.

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.