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Dynamic Prediction in Clinical Survival Analysis

Hans C. van Houwelingen and Hein Putter
Publisher: 
Chapman & Hall/CRC
Publication Date: 
2012
Number of Pages: 
234
Format: 
Hardcover
Series: 
Monographs in Statistics and Applied Probability 123
Price: 
89.95
ISBN: 
9781439835333
We do not plan to review this book.

Prognostic models for survival data using (clinical) information available at baseline, based on the Cox model
The special nature of survival data
Introduction
Basic statistical concepts
Predictive use of the survival function
Additional remarks
Cox regression model
The hazard function
The proportional hazards model
Fitting the Cox model
Example: Breast Cancer II
Extensions of the data structure
Alternative models
Additional remarks
Measuring the predictive value of a Cox model
Introduction
Visualizing the relation between predictor and survival
Measuring the discriminative ability
Measuring the prediction error
Dealing with overfitting
Cross-validated partial likelihood
Additional remarks
Calibration and revision of Cox models
Validation by calibration
Internal calibration
External calibration
Model revision
Additional remarks

Prognostic models for survival data using (clinical) information available at baseline, when the proportional hazards assumption of the Cox model is violated
Mechanisms explaining violation of the Cox model
The Cox model is just a model
Heterogeneity
Measurement error in covariates
Cause specific hazards and competing risks
Additional remarks
Non-proportional hazards models
Cox model with time-varying coefficients
Models inspired by the frailty concept
Enforcing parsimony through reduced rank models
Additional remarks
Dealing with non-proportional hazards
Robustness of the Cox model
Obtaining dynamic predictions by landmarking
Additional remarks

Dynamic prognostic models for survival data using time-dependent information
Dynamic predictions using biomarkers
Prediction in a dynamic setting
Landmark prediction model
Application
Additional remarks
Dynamic prediction in multi-state models
Multi-state models in clinical applications
Dynamic prediction in multi-state models
Application
Additional remarks
Dynamic prediction in chronic disease
General description
Exploration of the EORTC breast cancer data set
Dynamic prediction models for breast cancer
Dynamic assessment of "cure"
Additional remarks

Dynamic prognostic models for survival data using genomic data
Penalized Cox models
Introduction
Ridge and lasso
Application to Data Set 3
Adding clinical predictors
Additional remarks
Dynamic prediction based on genomic data
Testing the proportional hazards assumption
Landmark predictions
Additional remarks

Appendices
Data sets
Advanced ovarian cancer
Chronic Myeloid Leukemia (CML)
Breast Cancer I (NKI)
Gastric Cancer
Breast Cancer II (EORTC)
Acute Lymphatic Leukemia (ALL)
B Software and website
R packages used
The dynpred package
Additional remarks

References
Index

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