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Medical risk prediction models
Paperback
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- Book 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.
- Product Details
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- ISBN
- 9780367673734
- Format
- Paperback
- Publisher
- Chapman & Hall/CRC, (29 August 2022)
- Number of Pages
- 290
- Weight
- 580 grams
- Language
- English
- Dimensions
- 234 x 233 x 20 mm
- Series:
- See all books in this series
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