Medical Risk Prediction Models With Ties to Machine Learning

You must be logged in to access this title.

Sign up now

Already a member? Log in

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


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.

Book details

Series:
Chapman & Hall/CRC Biostatistics Series
Author:
Thomas A. Gerds, Michael W. Kattan
ISBN:
9780429764240
Related ISBNs:
9781138384484, 9780367673734, 9781138384477
Publisher:
CRC Press
Pages:
290
Reading age:
Not specified
Includes images:
No
Date of addition:
2021-02-01
Usage restrictions:
Copyright
Copyright date:
2022
Copyright by:
N/A 
Adult content:
No
Language:
English
Categories:
Mathematics and Statistics, Medicine, Nonfiction