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Books > Medicine > General issues > Public health & preventive medicine > Epidemiology & medical statistics
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Medical Risk Prediction Models - With Ties to Machine Learning (Paperback)
Loot Price: R1,547
Discovery Miles 15 470
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Medical Risk Prediction Models - With Ties to Machine Learning (Paperback)
Series: Chapman & Hall/CRC Biostatistics Series
Expected to ship within 9 - 15 working days
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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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