Medical Risk Prediction Models: With Ties to Machine Learning (Chapman & Hall/CRC Biostatistics Series) - Softcover
Book 115 of 151: Chapman & Hall/CRC Biostatistics
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.
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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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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? This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780367673734
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