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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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
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.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Medical Risk Prediction Models: With Ties to Machine Learning (Chapman & Hall/CRC Biostatistics Series)
Editore:
Chapman and Hall/CRC 2020-12-18, 2020
ISBN 10: 113838447X
ISBN 13: 9781138384477
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Medical Risk Prediction Models
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 . Codice articolo 395504665
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Medical Risk Prediction Models
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Chapman And Hall/CRC Feb 2021, 2021
ISBN 10: 113838447X
ISBN 13: 9781138384477
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 314 pp. Englisch. Codice articolo 9781138384477
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Medical Risk Prediction Models : With Ties to Machine Learning
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Medical Risk Prediction Models: With Ties to Machine Learning (Chapman & Hall/CRC Biostatistics Series)
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Medical Risk Prediction Models : With Ties to Machine Learning
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Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Codice articolo 9781138384477
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Medical Risk Prediction Models
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Medical Risk Prediction Models: With Ties to Machine Learning
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Hardcover. Condizione: Brand New. 1st edition. 290 pages. 9.25x6.25x1.00 inches. In Stock. This item is printed on demand. Codice articolo __113838447X
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