Medical Risk Prediction Models: With Ties to Machine Learning

Gerds, Thomas A.

ISBN 10: 0367673738 / ISBN 13: 9780367673734

Editore: Chapman and Hall/CRC 2022-08, 2022

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Codice articolo 6666-IUK-9780367673734

Dati bibliografici

• Titolo: Medical Risk Prediction Models: With Ties to Machine Learning
• Editore: Chapman and Hall/CRC 2022-08
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 0367673738
• ISBN 13: 9780367673734
• Rilegatura: PF
• Condizione: New

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

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.