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商品詳細
Medical Risk Prediction Models: With Ties to Machine Learning.
・ISBN 978-1-138-38447-7 hard GB£ 171.99
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| 著者・編者 | Gerds, Thomas A. / Kattan, Michael W., |
|---|---|
| シリーズ | (Chapman & Hall/CRC Biostatistics Series) |
| 出版社 | (CRC Press, UK) |
| 出版年月 | 2021 |
| ページ数 | 312 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A00-43383＞ |
解説
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.