Medical Risk Prediction Models
With Ties to Machine Learning
Thomas A. Gerds, Michael W. Kattan
- 312 Seiten
- English
- ePUB (handyfreundlich)
- Über iOS und Android verfügbar
Medical Risk Prediction Models
With Ties to Machine Learning
Thomas A. Gerds, Michael W. Kattan
Über dieses Buch
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:
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- All you need to know to correctly make an online risk calculator from scratch
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- Discrimination, calibration, and predictive performance with censored data and competing risks
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- R-code and illustrative examples
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- Interpretation of prediction performance via benchmarks
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- 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.