<p><strong>Medical Risk Prediction Models: With Ties to Machine Learning</strong> 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.</p>
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<p>Features:</p>
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</p><li>All you need to know to correctly make an online risk calculator from scratch</li>
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</p><li>Discrimination, calibration, and predictive performance with censored data and competing risks</li>
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</p><li>R-code and illustrative examples</li>
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</p><li>Interpretation of prediction performance via benchmarks</li>
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</p><li>Comparison and combination of rival modeling strategies via cross-validation</li>
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</b><p><b>Thomas A. Gerds</b> 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.</p>
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</b><p><b>Michael W. Kattan</b> 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.</p>