Survival and Event History Analysis (eBook)
XVIII, 541 Seiten
Springer New York (Verlag)
978-0-387-68560-1 (ISBN)
The aim of this book is to bridge the gap between standard textbook models and a range of models where the dynamic structure of the data manifests itself fully. The common denominator of such models is stochastic processes. The authors show how counting processes, martingales, and stochastic integrals fit very nicely with censored data. Beginning with standard analyses such as Kaplan-Meier plots and Cox regression, the presentation progresses to the additive hazard model and recurrent event data. Stochastic processes are also used as natural models for individual frailty; they allow sensible interpretations of a number of surprising artifacts seen in population data.
The stochastic process framework is naturally connected to causality. The authors show how dynamic path analyses can incorporate many modern causality ideas in a framework that takes the time aspect seriously.
To make the material accessible to the reader, a large number of practical examples, mainly from medicine, are developed in detail. Stochastic processes are introduced in an intuitive and non-technical manner. The book is aimed at investigators who use event history methods and want a better understanding of the statistical concepts. It is suitable as a textbook for graduate courses in statistics and biostatistics.
The aim of this book is to bridge the gap between standard textbook models and a range of models where the dynamic structure of the data manifests itself fully. The common denominator of such models is stochastic processes. The authors show how counting processes, martingales, and stochastic integrals fit very nicely with censored data. Beginning with standard analyses such as Kaplan-Meier plots and Cox regression, the presentation progresses to the additive hazard model and recurrent event data. Stochastic processes are also used as natural models for individual frailty; they allow sensible interpretations of a number of surprising artifacts seen in population data.The stochastic process framework is naturally connected to causality. The authors show how dynamic path analyses can incorporate many modern causality ideas in a framework that takes the time aspect seriously. To make the material accessible to the reader, a large number of practical examples, mainly frommedicine, are developed in detail. Stochastic processes are introduced in an intuitive and non-technical manner. The book is aimed at investigators who use event history methods and want a better understanding of the statistical concepts. It is suitable as a textbook for graduate courses in statistics and biostatistics.
Preface 7
Contents 10
Chapter 1 An introduction to survival and event history analysis 18
Chapter 2 Stochastic processes in event history analysis 57
Chapter 3 Nonparametric analysis of survival and event history data 84
Chapter 4 Regression models 146
Chapter 5 Parametric counting process models 221
Chapter 6 Unobserved heterogeneity: The odd effects of frailty 244
Chapter 7 Multivariate frailty models 284
Chapter 8 Marginal and dynamic models for recurrent events and clustered survival data 314
Chapter 9 Causality 360
Chapter 10 First passage time models: Understanding the shape of the hazard rate 399
Chapter 11 Diffusion and L évy process models for dynamic frailty 437
Appendix A Markov processes and the product-integral 468
Appendix B Vector-valued counting processes, martingales and stochastic integrals 506
References 509
Author index 531
Index 539
| Erscheint lt. Verlag | 16.9.2008 |
|---|---|
| Reihe/Serie | Statistics for Biology and Health | Statistics for Biology and Health |
| Zusatzinfo | XVIII, 540 p. |
| Verlagsort | New York |
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Informatik |
| Mathematik / Informatik ► Mathematik ► Statistik | |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| Medizin / Pharmazie ► Allgemeines / Lexika | |
| Technik | |
| Wirtschaft ► Betriebswirtschaft / Management | |
| Schlagworte | Causality • counting process • counting processes • Cox Regression Model • frailty models • Markov process • Martingale • Multivariate Survival Data • Quality Control, Reliability, Safety and Risk • Radiologieinformationssystem • Sage • Statistics • Stochastic process • Stochastic Processes |
| ISBN-10 | 0-387-68560-X / 038768560X |
| ISBN-13 | 978-0-387-68560-1 / 9780387685601 |
| Haben Sie eine Frage zum Produkt? |
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