Advances in survival analysis /

• Άλλοι συγγραφείς: Balakrishnan, N., 1956- (Επιμελητής έκδοσης), Rao, C. Radhakrishna (Calyampudi Radhakrishna), 1920- (Επιμελητής έκδοσης)
• Μορφή: Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Amsterdam ; Boston : Elsevier North-Holland, 2004.
• Έκδοση: 1st ed.
• Σειρά: Handbook of statistics (Amsterdam, Netherlands) ; v. 23.
• Θέματα: Failure time data analysis.; Survival Analysis.; MATHEMATICS > Probability & Statistics > General.; Overlevingsanalyse.; Analyse des temps entre défaillances.; Analyse de survie (Statistique); Modèle statistique.; Statistik; Ereignisdatenanalyse; Industrie; Electronic books.; Aufsatzsammlung.

The book covers all important topics in the area of Survival Analysis. Each topic has been covered by one or more chapters written by internationally renowned experts. Each chapter provides a comprehensive and up-to-date review of the topic. Several new illustrative examples have been used to demons...

Πίνακας περιεχομένων:

• Cover
• Table of Contents
• Preface
• Contributors
• PART I: GENERAL METHODOLOGY
• Chapter 1. Evaluation of the Performance of Survival Analysis Models: Discrimination and Calibration Measures
• 1. Introduction
• 2. Discrimination index
• 3. Calibration measures in survival analysis
• Appendix A
• References
• Chapter 2. Discretizing a Continuous Covariate in Survival Studies
• 1. Introduction
• 2. Techniques based on the Cox model with a single covariate
• 3. Extensions of Contal and O'Quigley's approach
• 4. Discussion
• Acknowledgements
• References
• Chapter 3. On Comparison of Two Classification Methods with Survival Endpoints
• 1. Introduction
• 2. Degree of separation index
• 3. Estimation and inference procedures
• 4. Distribution property of test statistics under the null hypothesis
• 5. Application examples
• 6. Discussion and conclusion
• Acknowledgement
• References
• Chapter 4. Time Varying Effects in Survival Analysis
• 1. Time varying effects in survival analysis
• 2. Estimation for proportional or additive models
• 3. Testing in proportional and additive hazards models
• 4. Survival with malignant melanoma
• 5. Discussion
• Acknowledgement
• References
• Chapter 5. Kaplan-Meier Integrals
• 1. Introduction
• 2. The SLLN
• 3. The CLT
• 4. Bias
• 5. The jackknife
• 6. Censored correlation and regression
• 7. Conclusions
• References
• PART II: CENSORED DATA AND INFERENCE
• Chapter 6. Statistical Analysis of Doubly Interval-Censored Failure Time Data
• 1. Introduction
• 2. Nonparametric estimation of a distribution function
• 3. Semiparametric regression analysis
• 4. Nonparametric comparison of survival functions
• 5. Discussion and future researches
• References
• Chapter 7. The Missing Censoring-Indicator Model of Random Censorship
• 1. Introduction
• 2. Overview of the estimators of a survival function
• 3. Semiparametric estimation in the MCI model
• 4. Conclusion
• Acknowledgement
• References
• Chapter 8. Estimation of the Bivariate Survival Function with Generalized Bivariate Right Censored Data Structures
• 1. Introduction
• 2. Modeling the censoring mechanism
• 3. Constructing an initial mapping from full data estimating functions to observed data estimating functions
• 4. Generalized Dabrowska's estimator
• 5. Orthogonalized estimating function and corresponding estimator
• 6. Simulations
• 7. Discussion
• Appendix A
• References
• Chapter 9. Estimation of Semi-Markov Models with Right-Censored Data
• 1. Introduction
• 2. Definition of the estimators
• 3. Asymptotic distribution of the estimators
• 4. Generalization to models with covariates
• 5. Discussion
• References
• PART III: TRUNCATED DATA AND INFERENCE
• Chapter 10. Nonparametric Bivariate Estimation with Randomly Truncated Observations
• 1. Introduction
• 2. Estimation of the bivariate distribution function
• 3. Estimation of bivariate hazard
• 4. Biv.