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Medical Risk Prediction Models: With Ties to Machine Learning (Chapman & Hall/CRC Biostatistics Series) - Softcover
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.
"synopsis" may belong to another edition of this title.
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.
"About this title" may belong to another edition of this title.
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MEDICAL RISK PREDICTION MODELS: WITH TIE
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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 (Chapman & Hall/CRC Biostatistics Series)
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Medical Risk Prediction Models
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Taschenbuch. Condition: 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. Seller Inventory # 9780367673734
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Medical Risk Prediction Models (Paperback)
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ISBN 10: 0367673738
ISBN 13: 9780367673734
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Paperback. Condition: new. Paperback. 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 patients 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. The backbone of medical decision making is prediction. Statistical prediction models can help in medical decision making. This book takes the viewpoint of the single patient and asks what does it mean that a risk prediction model performs well for a single individual? Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9780367673734
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