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.
