Handbook of Survival Analysis (Chapman & Hall/CRC Handbooks of Modern Statistical Methods)

Handbook Of Survival Analysis Presents Modern Techniques And Research Problems In Lifetime Data Analysis. This Area Of Statistics Deals With Time-to-event Data That Is Complicated By Censoring And The Dynamic Nature Of Events Occurring In Time. With Chapters Written By Leading Researchers In The Field, The Handbook Focuses On Advances In Survival Analysis Techniques, Covering Classical And Bayesian Approaches. It Gives A Complete Overview Of The Current Status Of Survival Analysis And Should Inspire Further Research In The Field. Accessible To A Wide Range Of Readers, The Book Provides An Introduction To Various Areas In Survival Analysis For Graduate Students And Novices, A Reference To Modern Investigations Into Survival Analysis For More Established Researchers, A Text Or Supplement For A Second Or Advanced Course In Survival Analysis, And A Useful Guide To Statistical Methods For Analyzing Survival Data Experiments For Practicing Statisticians--publisher's Description. I. Regression Models For Right Censoring -- Cox Regression Model -- Bayesian Analysis Of The Cox Model -- Alternatives To The Cox Model -- Transformation Models -- High-dimensional Regression Models -- Cure Models -- Causal Models -- Ii. Competing Risks -- Classical Regression Models For Competing Risks -- Bayesian Regression Models For Competing Risks -- Pseudo-value Regression Models -- Binomial Regression Models -- Regression Models In Bone Marrow Transplantation: A Case Study -- Iii. Model Selection And Validation -- Classical Model Selection -- Bayesian Model Selection -- Model Selection For High-dimensional Models -- Robustness Of Proportional Hazards Regression -- Iv. Other Censoring Schemes -- Nested Case-control And Case-cohort Studies -- Interval Censoring -- Current Status Data: An Illustration With Data On Avalanche Victims -- V. Multivariate/multistate Models -- Multistate Models -- Landmarking -- Frailty Models -- Bayesian Analysis Of Frailty Models -- Copula Models. Edited By John P. Klein, Hans C. Van Houwelingen, Joseph G. Ibrahim, Thomas Scheike. Includes Bibliographical References And Index. "This handbook focuses on the analysis of lifetime data arising from the biological and medical sciences. It deals with semiparametric and nonparametric methods. For investigators new to this field, the book provides an overview of the topic along with examples of the methods discussed. It presents both classical methods and modern Bayesian approaches to the analysis of data"-- "Preface This volume examines modern techniques and research problems in the analysis of life time data analysis. This area of statistics deals with time to event data which is complicated not only by the dynamic nature of events occurring in time but by censoring where some events are not observed directly but rather they are known to fall in some interval or range. Historically survival analysis is one of the oldest areas of statistics dating its origin to classic life table construction begun in the 1600's. Much of the early work in this area involved constructing better life tables and long tedious extensions of non-censored nonparametric estimators. Modern survival analysis began in the late 1980's with pioneering work by Odd Aalen on adapting classical Martingale theory to these more applied problems. Theory based on these counting process martingales made the development of techniques for censored and truncated data in most cases easier and opened the door to both Bayesian and classical statistics for a wide range of problems and applications. In this volume we present a series of papers which provide an introduction to the advances in survival analysis techniques in the past thirty years. These papers can serve four complimentary purposes. First, they provide an introduction to various areas in survival analysis for graduates students and other new researchers to this eld. Second, they provide a reference to more established investigators in this area of modern investigations into survival analysis. Third, with a bit of supplementation on counting process theory this volume is useful as a text for a second or advanced course in survival analysis. We have found that the instructor of such a course can pick and chose papers in areas he/she deem most useful to the"-- "Preface This volume examines modern techniques and research problems in the analysis of life time data analysis. This area of statistics deals with time to event data which is complicated not only by the dynamic nature of events occurring in time but by censoring where some events are not observed directly but rather they are known to fall in some interval or range. Historically survival analysis is one of the oldest areas of statistics dating its origin to classic life table construction begun in the 1600's. Much of the early work in this area involved constructing better life tables and long tedious extensions of non-censored nonparametric estimators. Modern survival analysis began in the late 1980's with pioneering work by Odd Aalen on adapting classical Martingale theory to these more applied problems. Theory based on these counting process martingales made the development of techniques for censored and truncated data in most cases easier and opened the door to both Bayesian and classical statistics for a wide range of problems and applications. In this volume we present a series of papers which provide an introduction to the advances in survival analysis techniques in the past thirty years. These papers can serve four complimentary purposes. First, they provide an introduction to various areas in survival analysis for graduates students and other new researchers to this eld. Second, they provide a reference to more established investigators in this area of modern investigations into survival analysis. Third, with a bit of supplementation on counting process theory this volume is useful as a text for a second or advanced course in survival analysis. We have found that the instructor of such a course can pick and chose papers in areas he/she deem most useful to the"-- Provided by publisher Front Cover 1 Handbook of Survival Analysis 4 Copyright 5 Table of Contents 6 Preface 9 About the Editors 10 List of Contributors 12 Part I: Regression Models for Right Censoring 16 1. Cox Regression Model 20 2. Bayesian Analysis of the Cox Model 42 3. Alternatives to the Cox Model 64 4. Transformation Models 91 5. High-Dimensional Regression Models 106 6. Cure Models 126 7. Causal Models 148 Part II: Competing Risks 166 8. Classical Regression Models for Competing Risks 170 9. Bayesian Regression Models for Competing Risks 191 10. Pseudo-Value Regression Models 211 11. Binomial Regression Models 232 12. Regression Models in Bone Marrow Transplantation – A Case Study 254 Part III: Model Selection and Validation 274 13. Classical Model Selection 276 14. Bayesian Model Selection 295 15. Model Selection for High-Dimensional Models 310 16. Robustness of Proportional Hazards Regression 332 Part IV: Other Censoring Schemes 349 17. Nested Case-Control and Case-Cohort Studies 351 18. Interval Censoring 376 19. Current Status Data: An Illustration with Data on Avalanche Victims 398 Part V: Multivariate/Multistate Models 420 20. Multistate Models 423 21. Landmarking 446 22. Frailty Models 462 23. Bayesian Analysis of Frailty Models 479 24. Copula Models 493 25. Clustered Competing Risks 515 26. Joint Models of Longitudinal and Survival Data 527 27. Familial Studies 552 Part VI: Clinical Trials 572 28. Sample Size Calculations for Clinical Trials 574 29. Group Sequential Designs for Survival Data 598 30. Inference for Paired Survival Data 617 Back Cover 635