Medical risk prediction models : with ties to machine learning

著者

    • Gerds, Thomas A.
    • Kattan, Michael W.

書誌事項

Medical risk prediction models : with ties to machine learning

Thomas A. Gerds, Michael W. Kattan

(Chapman & Hall/CRC biostatistics series)

CRC Press, c2021

  • : hbk

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注記

Includes bibliographical references and index

内容説明・目次

内容説明

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 Thomas A. Gerds is a professor at the Biostatistics Unit at the University of Copenhagen and is affiliated with the Danish Heart Foundation. He is the author of several R-packages on CRAN and has taught statistics courses to non-statisticians for many years. Michael W. 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.

目次

Software. 2. I am going to make a prediction model. What do I need to know? 3. Regression model. 4. How should I prepare for modeling? 5. I am ready to build a prediction model. 7. Does my model predict accurately? 7. How do I decide between rival models? 8. Can't the computer just take care of all of this? 9. Things you might have expected in our book.

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詳細情報

  • NII書誌ID(NCID)
    BC0864499X
  • ISBN
    • 9781138384477
  • 出版国コード
    us
  • タイトル言語コード
    eng
  • 本文言語コード
    eng
  • 出版地
    Boca Raton
  • ページ数/冊数
    xxi, 290 p.
  • 大きさ
    24 cm
  • 親書誌ID
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