(Ebook) Advances in Survival Analysis 1st Edition by N Balakrishnan, C R Rao ISBN 0444500790 9780444500793

(Ebook) Advances in Survival Analysis 1st Edition by N Balakrishnan, C R Rao ISBN 0444500790 9780444500793

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ISBN 10: 0444500790 
ISBN 13: 9780444500793
Author: N Balakrishnan, C R Rao

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 demonstrate the methodologies developed. The book also includes an exhaustive list of important references in the area of Survival Analysis.

· Includes up-to-date reviews on many important topics.
· Chapters written by many internationally renowned experts.
· Some Chapters provide completely new methodologies and analyses.
· Includes some new data and methods of analyzing them.

(Ebook) Advances in Survival Analysis 1st Table of contents:

PART I: GENERAL METHODOLOGY
Chapter 1. Evaluation of the Performance of Survival Analysis Models: Discrimination and Calibration
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 D
1. Introduction
2. Modeling the censoring mechanism
3. Constructing an initial mapping from full data estimating functions to observed data estimating f
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. Bivariate density estimation
References
PART IV: HAZARD RATE ESTIMATION
Chapter 11. Lower Bounds for Estimating a Hazard
1. Introduction
2. Framework
3. Kullback information and Hellinger distances based on hazards
4. A general device to derive lower bounds for estimating a function
5. Lower bound for the rate of estimation of a hazard function with right censoring
6. Rate of convergence for the kernel estimator of the hazard function
Acknowledgement
Appendix A
References
Chapter 12. Non-Parametric Hazard Rate Estimation under Progressive Type-II Censoring
1. Introduction
2. Smoothing cumulative hazard rate estimator
3. Asymptotics
4. Simulation study
Appendix A: Technical results for the mean
Appendix B: Technical results for the variance
References
PART V: COMPARISON OF SURVIVAL CURVES
Chapter 13. Statistical Tests of the Equality of Survival Curves: Reconsidering the Options
1. Introduction
2. Underlying alternative hypothesis and assumptions
3. An overview of available tests
4. Hypothesis testing and statistical computer packages
5. Applications to papers from major medical journal
6. Suggested guidelines
7. Discussion and conclusions
References
Chapter 14. Testing Equality of Survival Functions with Bivariate Censored Data: A Review
1. Introduction
2. Testing H0 with uncensored paired data
3. Within-pair difference tests with censored data
4. Pooled sample tests with censored data
5. Testing H0 when there are missing data
6. Overview
References
Chapter 15. Statistical Methods for the Comparison of Crossing Survival Curves
1. Introduction
2. The modified Kolmogorov–Smirnov test
3. A Levene-type test
4. Linear rank tests
References
PART VI: COMPETING RISKS AND ANALYSIS
Chapter 16. Inference for Competing Risks
1. Introduction
2. Basic quantities
3. Univariate estimation
4. Inference based on the crude hazard rates
5. Tests based on the cumulative incidence function
6. Regression techniques based on the cumulative hazard function
7. Discussion
Acknowledgement
References
Chapter 17. Analysis of Cause-Specific Events in Competing Risks Survival Data
1. Introduction
2. Competing risks analysis based on cause specific hazard functions
3. Competing risks analysis based on cumulative incidence functions
4. Examples: Competing risks analysis of events after breast cancer treatment
5. Summary
Acknowledgements
References
Chapter 18. Analysis of Progressively Censored Competing Risks Data
1. Introduction
2. Model Description and notation
3. Estimation
4. Confidence intervals
5. Bayesian analysis
6. Simulation study
7. Numerical example
8. Some generalizations and extensions
9. Conclusions
References
Chapter 19. Marginal Analysis of Point Processes with Competing Risks
1. Introduction
2. Rate functions for point processes
3. Point processes with terminal events
4. Application to a breast cancer trial
5. Discussion
Acknowledgements
References
PART: VII: PROPORTIONAL HAZARDS MODEL AND ANALYSIS
Chapter 20. Categorical Auxiliary Data in the Discrete Time Proportional Hazards Model
1. Introduction
2. The standard and joint discrete time proportional hazards models
3. Specification of the survival model and censoring
4. Discretizing continuous auxiliary data
5. Joint models Recurrent events predicting survival
6. Other scenarios for censoring and survival
7. Discussion
Acknowledgements
Appendix A
References
Chapter 21. Hosmer and Lemeshow type Goodness-of-Fit Statistics for the Cox Proportional Hazards Mod
1. Introduction
2. The Hosmer and Lemeshow type test statistics
3. Necessity for time dependent indicator variables
4. Examples
5. Summary
Appendix A
Appendix B
Appendix C
References
Chapter 22. The Effects of Misspecifying Cox’s Regression Model on Randomized Treatment Group Comp
1. Introduction
2. Notation and statistics
3. Conditions for valid tests
4. Bias correction
5. Discussion
Acknowledgement
Appendix A: MATLAB code for computing statistical tests
References
Chapter 23. Statistical Modeling in Survival Analysis and Its Influence on the Duration Analysis
1. Introduction
2. The Cox or the proportional hazards model
3. Accelerated failure time model
4. Generalized proportional hazards model
5. Regression models with cross-effects of survival functions
6. Changing shape and scale models
7. Models with time-dependent regression coefficients
8. Additive hazards model and its generalizations
9. Remarks on parametric and semi-parametric estimation
References
PART VIII: ACCELERATED MODELS AND ANALYSIS
Chapter 24. Accelerated Hazards Model Method, Theory and Applications
1. Introduction
2. Estimation
3. Asymptotic results
4. Efficiency consideration
5. Model adequacy
6. Extensions
7. Implementation and application
8. Some remarks
References
Chapter 25. Diagnostics for the Accelerated Life Time Model of Survival Data
1. Introduction
2. The likelihood and estimating equations
3. A Gibbs-like estimation procedure
4. Diagnostic measures
5. The bootstrap procedure
6. Numerical examples
Acknowledgement
References
Chapter 26. Cumulative Damage Approaches Leading to Inverse Gaussian Accelerated Test Models
1. Inverse Gaussian as a lifetime or strength model
2. Inverse Gaussian accelerated test models
3. Estimation for the inverse Gaussian accelerated test models
4. Application of the inverse Gaussian accelerated test models to chloroprene exposure data
5. Conclusion
Acknowledgement
References
Chapter 27. On Estimating the Gamma Accelerated Failure Time Models
1. The failure time Gamma model
2. The maximum likelihood equations
3. The problem
4. The hybrid approximation
5. The SAS/IML subroutine NLPTR
6. Pediatric cancer data
7. Leukemia data
8. Concluding remarks
Acknowledgements
Appendix A: Fisher information matrix
References
PART IX: FRAILTY MODELS AND APPLICATIONS
Chapter 28. Frailty Model and its Application to Seizure Data
1. Introduction
2. Inference for the shared frailty model
3. The shared frailty model for recurrent events
4. Seizure data and its analysis
5. Concluding remarks
Acknowledgements
References
PART X: MODELS AND APPLICATIONS
Chapter 29. State Space Models for Survival Analysis
1. Introduction
2. The state space models and the generalized Bayesian approach
3. Stochastic modeling of the birth–death–immigration–illness–cure processes
4. A state space model for the birth–death–immigration–illness–cure processes
5. The multi-level Gibbs sampling procedures for the birth–death–immigration–illness–cure pr
6. The survival probabilities of normal and sick people
7. Some illustrative examples
8. Conclusions
References
Chapter 30. First Hitting Time Models for Lifetime Data
1. Introduction
2. The basic first hitting time model
3. Data for model estimation
4. A Wiener process with an inverse Gaussian first hitting time
5. A two-dimensional Wiener model for a marker and first hitting time
6. Longitudinal data
7. Additional first hitting time models
8. Other literature sources
Acknowledgements
References
Chapter 31. An Increasing Hazard Cure Model
1. Introduction
2. The model
3. Simulation study
4. Illustration
5. Conclusions and discussion
Acknowledgements
References
PART XI: MULTIVARIATE SURVIVAL DATA ANALYSIS
Chapter 32. Marginal Analyses of Multistage Data
1. Introduction
2. ExplainableŽ dependent censoring in survival analysis
3. Multistage models: Stage occupation probabilities and marginal transition hazards
4. Estimation of marginal waiting time distributions
5. Regression models for waiting time distributions
Appendix A: Modeling the censoring hazard using Aalen’s linear hazards model
References
Chapter 33. The Matrix-Valued Counting Process Model with Proportional Hazards for Sequential Surviv
1. Introduction
2. Introduction to multivariate survival methods
3. The matrix valued counting process framework
4. Matrix valued counting process framework with repeated measures data
5. Estimation
6. Example
7. Discussion
Appendix A
References
Further reading
PART XII: RECURRENT EVENT DATA ANALYSIS
Chapter 34. Analysis of Recurrent Event Data
1. Introduction
2. Notation and basic functions of interest
3. Semiparametric models for recurrent event data
4. Nonparametric estimation of the recurrent event survival and distribution functions
5. Conclusion
Acknowledgement
References
PART XIII: CURRENT STATUS DATA ANALYSIS
Chapter 35. Current Status Data Review, Recent Developments and Open Problems
1. Introduction
2. Motivating examples
3. Simple current status data
4. Different sampling schemes
5. Complex outcome processes
6. Conclusion
References
PART XIV: DISEASE PROGRESSION ANALYSIS
Chapter 36. Appraisal of Models for the Study of Disease Progression in Psoriatic Arthritis
1. Introduction
2. Data
3. Markov models
4. Poisson and negative binomial models
5. Discussion
Appendix A: Formulas for the estimated transition probabilities
References
PART XV: GENE EXPRESSIONS AND ANALYSIS
Chapter 37. Survival Analysis with Gene Expression Arrays
1. Introduction
2. Methods
3. Results
4. Discussion
Appendix A
References
PART XVI: QUALITY OF LIFE ANALYSIS
Chapter 38. Joint Analysis of Longitudinal Quality of Life and Survival Processes
1. Introduction
2. Presentation of the clinical trial: QoL instruments and data
3. Preliminary analysis
4. Time to QoL deterioration
5. Semi-Markovian multi-state model
6. Joint distribution of QoL and survival–dropout processes
7. Discussion
References
PART XVII: FLOWGRAPH MODELS AND APPLICATIONS
Chapter 39. Modelling Survival Data using Flowgraph Models
1. Series flowgraph model: HIV blood transfusion data
2. Data analysis of HIV/AIDS data
3. Converting flowgraph MGFs to densities
4. Likelihood construction in flowgraph models
5. Parametric assumptions
6. Parallel flowgraph models
7. Loop flowgraph models
8. A systematic procedure for solving flowgraphs
9. Data analysis for diabetic retinopathy data
10. Summary
References
PART XVIII: REPAIR MODELS AND ANALYSIS
Chapter 40. Nonparametric Methods for Repair Models
1. Introduction
2. General repair models
3. Estimation in the DHS model
4. Estimation in the BBS model
5. A two-sample test in the BBS model
6. Goodness of-fit-tests in the BBS model
7. Testing the minimal repair assumption in the BBS model
Acknowledgement

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