» Classical Competing Risks
Rating: 5.0 / 5.00 (2 reviews)
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Manufacturer: Chapman & Hall/CRC
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Classical Competing Risks Details
Binding: HardcoverDewey Decimal Number: 519.2
EAN: 9781584881759
ISBN: 1584881755
Label: Chapman & Hall/CRC
Manufacturer: Chapman & Hall/CRC
Number Of Items: 1
Number Of Pages: 200
Publication Date: 2001-05-11
Publisher: Chapman & Hall/CRC
Studio: Chapman & Hall/CRC
- Competing Risks: A Practical Perspective (Statistics in Practice)
- Bayesian Computation with R (Use R)
- The Elements of Statistical Learning
- Modeling Survival Data: Extending the Cox Model (Statistics for Biology and Health)
- The Statistical Analysis of Failure Time Data (Wiley Series in Probability and Statistics)
Classical Competing Risks Reviews
Customer Rating:Summary: first good book on this topic since David and Moeschberger in 1978
Comment: Crowder has written an up-to-date text on an important problem in health science and medical research. The first good monograph on this subject was by David and Moeschberger in 1978 and no text devoted to this topic had been written until now. People get sick or die or hardware fails due to one of several possible causes. In the competing risks model these causes are all given probabilities of occurrence over time and "compete" to be the first to occur and thus cause the event.
The mathematics of competing risks is very much the same as the mathematics of survival analysis but instead of a single time to event curve there are many. For data analysis, one must be able to get data that includes not only the time of occurrence of the failure but also which of a list of possible causes the event is attributed to (the list of "competing" risks).
Crowder's text is introductory and reviews a lot of the basics of survival analysis and likelihood inference. Hazard functions and survival curves are introduced as are sub-survival curves and sub-hazard functions. The nonparametric Kaplan-Meier approach to survival analysis is presented as is the semiparametric Cox proportional hazards model. The important issue of parameter identifiability is given its proper place of importance.
The first seven chapters are written at an elementary to intermediate level that should be understandable to the undergraduate or graduate student taking this course. However, Chapter 8 deals with the modern and powerful counting process (martingale) approach to survival analysis and is more difficult to read. Chapter 8 has more of the flavor of an advanced probability topic and is suitable for graduate students who have taken that first advanced probability course.
Customer Rating:
Summary: first good text on this since David and Moeschberger in 1978
Comment: Crowder has written an up-to-date text on an important problem in health science and medical research. The first good monograph on this subject was by David and Moeschberger in 1978 and no text devoted to this topic had been written until now. People get sick or die or hardware fails due to one of several possible causes. In the competing risks model these causes are all given probabilities of occurrence over time and "compete" to be the first to occur and thus cause the event.
The mathematics of competing risks is very much the same as the mathematics of survival analysis but instead of a single time to event curve there are many. For data analysis, one must be able to get data that includes not only the time of occurrence of the failure but also which of a list of possible causes the event is attributed to (the list of "competing" risks).
Crowder's text is introductory and reviews a lot of the basics of survival analysis and likelihood inference. Hazard functions and survival curves are introduced as are sub-survival curves and sub-hazard functions. The nonparametric Kaplan-Meier approach to survival analysis is presented as is the semiparametric Cox proportional hazards model. The important issue of parameter identifiability is given its proper place of importance.
The first seven chapters are written at an elementary to intermediate level that should be understandable to the undergraduate or graduate student taking this course. However, Chapter 8 deals with the modern and powerful counting process (martingale) approach to survival analysis and is more difficult to read. Chapter 8 has more of the flavor of an advanced probability topic and is suitable for graduate students who have taken that first advanced probability course.
Editorial Review for Classical Competing Risks:
If something can fail, it can often fail in one of several ways and sometimes in more than one way at a time. There is always some cause of failure, and almost always, more than one possible cause. In one sense, then, survival analysis is a lost cause. The methods of Competing Risks have often been neglected in the survival analysis literature. Written by a leading statistician, Classical Competing Risks thoroughly examines the probability framework and statistical analysis of data of Competing Risks. The author explores both the theory of the subject and the practicalities of fitting the models to data. In a coherent, self-contained, and sequential account, the treatment moves from the bare bones of the Competing Risks setup and the associated likelihood functions through survival analysis using hazard functions. It examines discrete failure times and the difficulties of identifiability, and concludes with an introduction to the counting-process approach and the associated martingale theory.With a dearth of modern treatments on the subject and the importance of its methods, this book fills a long-standing gap in the literature with a carefully organized exposition, real data sets, numerous examples, and clear, readable prose. If you work with lifetime data, Classical Competing Risks presents a modern, comprehensive overview of the methodology and theory you need.