Description
Medical Risk Prediction Models
With Ties to Machine Learning
Chapman & Hall/CRC Biostatistics Series
Authors: Gerds Thomas A., Kattan Michael W.
Language: EnglishSubjects for Medical Risk Prediction Models:
Keywords
Prediction Time Horizon; Risk Prediction Model; competing-risks; Brier Score; discrimination; Cox Regression Model; modeling; OHSS; cross-validation; Regression Model; censored-data; Predictor Variables; calibration; Super Learner; predictive analytics; Learning Dataset; medical risk prediction; Prediction Performance; Cox regression models; Null Model; calibration plot; Random Forest; machine learning; Statistical Prediction Model; Antral Follicle Count; Continuous Predictor Variables; Predicted Risks; Landmark Time Point; Free PSA; Roc Curve; Regular Bootstrap; High AUC; Multiple Imputation Analysis; Complete Case Analysis; IDI; NRI
62.49 €
In Print (Delivery period: 14 days).
Add to cart the print on demand of Gerds Thomas A., Kattan Michael W.Publication date: 08-2022
Support: Print on demand
166.30 €
In Print (Delivery period: 14 days).
Add to cart the book of Gerds Thomas A., Kattan Michael W.Publication date: 02-2021
· 15.6x23.4 cm · Hardback
Description
/li>Contents
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/li>Biography
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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.
- 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.
Thomas A. Gerds is professor at the biostatistics unit at the University of Copenhagen. He is affiliated with the Danish Heart Foundation. He is author of several R-packages on CRAN and has taught statistics courses to non-statisticians for many years.
Michael 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.