Medical Risk Prediction Models : With Ties to Machine Learning, EPUB eBook

Medical Risk Prediction Models : With Ties to Machine Learning EPUB

Part of the Chapman & Hall/CRC Biostatistics Series series

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Description

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.

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