Survival Analysis
Techniques for Censored and Truncated Data
(Sprache: Englisch)
Applied statisticians in many fields must frequently analyze time to event data. While the statistical tools presented in this book are applicable to data from medicine, biology, public health, epidemiology, engineering, economics, and demography, the focus...
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Klappentext zu „Survival Analysis “
Applied statisticians in many fields must frequently analyze time to event data. While the statistical tools presented in this book are applicable to data from medicine, biology, public health, epidemiology, engineering, economics, and demography, the focus here is on applications of the techniques to biology and medicine. The analysis of survival experiments is complicated by issues of censoring, where an individual's life length is known to occur only in a certain period of time, and by truncation, where individuals enter the study only if they survive a sufficient length of time or individuals are included in the study only if the event has occurred by a given date. The use of counting process methodology has allowed for substantial advances in the statistical theory to account for censoring and truncation in survival experiments. This book makes these complex methods more accessible to applied researchers without an advanced mathematical background. The authors present the essence of these techniques, as well as classical techniques not based on counting processes, and apply them to data. Practical suggestions for implementing the various methods are set off in a series of Practical Notes at the end of each section. Technical details of the derivation of the techniques are sketched in a series of Technical Notes. This book will be useful for investigators who need to analyze censored or truncated life time data, and as a textbook for a graduate course in survival analysis. The prerequisite is a standard course in statistical methodology.
Applied statisticians in many fields frequently analyze time-to-event data. While the statistical tools presented in this book are applicable to data from medicine, biology, public health, epidemiology, engineering, economics and demography, the focus here is on applications of the techniques to biology and medicine.
The analysis of survival experiments is complicated by issues of censoring and truncation. The use of counting process methodology has allowed for substantial advances in the statistical theory to account for censoring and truncation in survival experiments. This book makes these complex techniques accessible to applied researchers without the advanced mathematical background. The authors present the essentials of these techniques, as well as classical techniques not based on counting processes, and apply them to data.
The second edition contains some new material as well as solutions to the odd-numbered revised exercises. New material consists of a discussion of summary statistics for competing risks probabilities in Chapter 2 and the estimation process for these probabilities in Chapter 4. A new section on tests of the equality of survival curves at a fixed point in time is added in Chapter 7. In Chapter 8 an expanded discussion is presented on how to code covariates and a new section on discretizing a continuous covariate is added. A new section on Lin and Ying's additive hazards regression model is presented in Chapter 10. We now proceed to a general discussion of the usefulness of this book incorporating the new material with that of the first edition.akes these complex techniques accessible to applied researchers without the advanced mathematical background. The authors present the essentials of these techniques, as well as classical techniques not based on counting processes, and apply them to data.
The second edition contains some new material as well as solutions to the odd-numbered revised exercises. New material consists of a discussion of
The analysis of survival experiments is complicated by issues of censoring and truncation. The use of counting process methodology has allowed for substantial advances in the statistical theory to account for censoring and truncation in survival experiments. This book makes these complex techniques accessible to applied researchers without the advanced mathematical background. The authors present the essentials of these techniques, as well as classical techniques not based on counting processes, and apply them to data.
The second edition contains some new material as well as solutions to the odd-numbered revised exercises. New material consists of a discussion of summary statistics for competing risks probabilities in Chapter 2 and the estimation process for these probabilities in Chapter 4. A new section on tests of the equality of survival curves at a fixed point in time is added in Chapter 7. In Chapter 8 an expanded discussion is presented on how to code covariates and a new section on discretizing a continuous covariate is added. A new section on Lin and Ying's additive hazards regression model is presented in Chapter 10. We now proceed to a general discussion of the usefulness of this book incorporating the new material with that of the first edition.akes these complex techniques accessible to applied researchers without the advanced mathematical background. The authors present the essentials of these techniques, as well as classical techniques not based on counting processes, and apply them to data.
The second edition contains some new material as well as solutions to the odd-numbered revised exercises. New material consists of a discussion of
Inhaltsverzeichnis zu „Survival Analysis “
Examples of Survival Data.- Basic Quantities and Models.- Censoring and Truncation.- Nonparametric Estimation of Basic Quantities for Right-Censored and Left-Truncated Data.- Estimation of Basic Quantities for Other Sampling Schemes.- Topics in Univariate Estimation.- Hypothesis Testing.- Semiparametric Proportional Hazards Regression with Fixed Covariates.- Refinements of the Semiparametric Proportional Hazards Model.- Additive Hazard Regression Models.- Regression Diagnostics.- Inference for Parametric Regression Models.- Multivariate Survival Analysis.
Bibliographische Angaben
- Autoren: John P. Klein , Melvin L. Moeschberger
- 2010, 2nd ed. 2003. Corr. 3rd printing, XVI, 538 Seiten, Maße: 21,1 x 28 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 1441929851
- ISBN-13: 9781441929853
Sprache:
Englisch
Pressezitat
...An excellent graduate-level text for a course in survival analysis. Students will definitely find the authors' systematic treatment of topics, clear discussions and derivations, and numerous detailed examples useful. This book is also a good reference source for practicing statisticians, biostatisticians, and public health professionals with a basic statistics and applied statistics background. Although the examples are biomedical in nature, most methods described in the book for time-to-event data are applicable to other fields, including engineering and economics, and the book should be useful for researchers in these disciplines. The authors use semiparametric and nonparametric methods extensively, and also discusses parametric models. The "Practical Notes" and "Theoretical Notes" provided in many sections are very attractive and give readers information and citations beyond the material in the text." (Technometrics, February 2004) "...The second edition of this book represents a well-organized and thorough exploration of many of the key ideas underlying survival analysis. The 18 datasets stemming from real-life experiences illustrate the concepts well. The Practical Notes and Theoretical Notes enhance understanding and provide the reader with guidance for further exploration and learning. This book is recommended as an up-to-date reference for statisticians and scientists engaged in the analysis of time-to-event data subject to censoring and/or truncation." (Journal of Biopharmaceutical Statistics, 2004)
"This book...offers an excellent course in survival analysis for Masters-level students or indeed for statisticians who wish to extend their knowledge of this subject...The authors treat the subject from a classical point of view and the mathematical level is compatible with that. A brief review of the alternative development of the subject through counting processes is given in Chapter 3 and further references and discussionare
... mehr
given in the theoretical notes that are part of each chapter. The subject is developed mathematically, but strong emphasis is placed on the practical implementation of the techniques. Included in each chapter are practical notes that extend the theoretical developments in the text and discuss relevant computer programs." (Short Book Reviews)
"The book's most significant and possibly controversial feature is that the materials are carefully presented with little technical difficulty involved...This is designed to fulfill the authors' goal of making complex methods accesesible to applied researchers without a strong mathematical background. The authors obviously have a lot of experience in teaching at this level and in consulting wtih various investigators. This book has plenty to offer for a one-or two-semester course for nonstatistics majors." (Journal of the American Statistical Association, September 2004)
From the reviews of the second edition:
"For a statistician in the pharmaceutical industry, the new material in this second edition, such as the competing risks section, is directly relevant and in sufficient detail to be useful in practice. The examples used throughout the book are based on medical data ... . the data are well chosen and sufficiently complex to illustrate the methods very well." (Kim Hawkins, Pharmaceutical Statistics, Issue 4, 2004)
"This is the second edition of a text whose first edition has already established a place for itself in the library of many applied statisticians, particularly biostatisticians. ... the book achieves a comprehensive coverage of the topic of survival analysis in a biomedical context, which serves the needs of students and researchers in a manner that is both interesting and mathematically satisfying. It deserves its place in the library of applied statisticians." (Gillian Z Heller, Statistics in Medicine, Vol. 23, 2004)
"This book deals with the analysis oftime to event data, focused on applications to biology and medicine. ... The book can be used as a text for a graduate level course on survival analysis and also for self study. ... Each new tool is presented through the treatment of a real example. More advanced topics are given in separate chapters or sections. ... The exposition is clear, the book is very well presented and makes pleasant reading." (Ricardo Maronna, Statistical Papers, Vol. 45 (3), 2004)
"Comprising 13 chapters and 5 appendixes, with 97 illustrations and several exercises at the end of each chapter, this book is an excellent graduate-level text for a course in survival analysis. Students will definitely find the authors' systematic treatment of topics, clear discussions and derivations, and numerous detailed examples useful. This book is also a good reference source for practicing statisticians, biostatisticians, and public health professionals with a basic statistics and applied statistics background." (Nalini Ravishanker, Technometrics, Vol. 46 (1), February, 2004)
From the reviews:
"Applied statisticians and researchers in medicine will find this book ... very useful. A basic level of statistical theory is necessary to understand the material of this well written book. ... In every chapter, there are challenging and easy problems. It is suited for a graduate level course in survival analysis. The statistical tables and reference contain recent material." (Ramalingam Shanmugam, Journal of Statistical Computation & Simulation, Vol. 74 (5), May, 2004)
"The book's most significant and possibly controversial feature is that the materials are carefully presented with little technical difficulty involved...This is designed to fulfill the authors' goal of making complex methods accesesible to applied researchers without a strong mathematical background. The authors obviously have a lot of experience in teaching at this level and in consulting wtih various investigators. This book has plenty to offer for a one-or two-semester course for nonstatistics majors." (Journal of the American Statistical Association, September 2004)
From the reviews of the second edition:
"For a statistician in the pharmaceutical industry, the new material in this second edition, such as the competing risks section, is directly relevant and in sufficient detail to be useful in practice. The examples used throughout the book are based on medical data ... . the data are well chosen and sufficiently complex to illustrate the methods very well." (Kim Hawkins, Pharmaceutical Statistics, Issue 4, 2004)
"This is the second edition of a text whose first edition has already established a place for itself in the library of many applied statisticians, particularly biostatisticians. ... the book achieves a comprehensive coverage of the topic of survival analysis in a biomedical context, which serves the needs of students and researchers in a manner that is both interesting and mathematically satisfying. It deserves its place in the library of applied statisticians." (Gillian Z Heller, Statistics in Medicine, Vol. 23, 2004)
"This book deals with the analysis oftime to event data, focused on applications to biology and medicine. ... The book can be used as a text for a graduate level course on survival analysis and also for self study. ... Each new tool is presented through the treatment of a real example. More advanced topics are given in separate chapters or sections. ... The exposition is clear, the book is very well presented and makes pleasant reading." (Ricardo Maronna, Statistical Papers, Vol. 45 (3), 2004)
"Comprising 13 chapters and 5 appendixes, with 97 illustrations and several exercises at the end of each chapter, this book is an excellent graduate-level text for a course in survival analysis. Students will definitely find the authors' systematic treatment of topics, clear discussions and derivations, and numerous detailed examples useful. This book is also a good reference source for practicing statisticians, biostatisticians, and public health professionals with a basic statistics and applied statistics background." (Nalini Ravishanker, Technometrics, Vol. 46 (1), February, 2004)
From the reviews:
"Applied statisticians and researchers in medicine will find this book ... very useful. A basic level of statistical theory is necessary to understand the material of this well written book. ... In every chapter, there are challenging and easy problems. It is suited for a graduate level course in survival analysis. The statistical tables and reference contain recent material." (Ramalingam Shanmugam, Journal of Statistical Computation & Simulation, Vol. 74 (5), May, 2004)
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