Medical Risk Prediction Models

With Ties to Machine Learning
1. Auflage 2022
ISBN: 978-0-367-67373-4
Verlag: Chapman and Hall/CRC

Buch, Englisch, 312 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 480 g

Reihe: Chapman & Hall/CRC Biostatistics Series

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.

Zielgruppe

Professional Practice & Development

Autoren/Hrsg.

Gerds, Thomas A.

Kattan, Michael W.

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Pflege Krankenpflege
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik

Inhaltsverzeichnis

- 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.

Über Autor(innen)

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.