An Introduction to Lifetime Data Analysis

Beschreibung

This book is oriented equally towards reliability, involving data analysis from engineering and the physical sciences, and survival analysis, usually related to data analysis in the biomedical sciences. Features extensive exercises using SAS and SYSTAT.
This textbook is an introduction to lifetime data analysis aimed primarily at undergraduate and beginning graduate statistics students needing exposure to major topics of the subject, but not yet specializing in a particular application area such as engineering or the biomedical sciences. Unlike other texts at the same level, the work is oriented equally towards reliability, which involves data analysis from engineering and the physical sciences, and survival analysis, usually related to data analysis in the biomedical sciences. Differing only in emphasis and terminology, the two areas—reliability and survival analysis—have essentially the same statistical subject matter: analysis of nonnegative random variables subject to censoring with possible covariates. Reliability places more emphasis on parametric models, which are covered in Chapter 6, and uses accelerated life much more than proportional hazards regression. Survival analysis employs semi-parametric models extensively, particularly Cox’s proportional hazards model, which is covered in Chapter 7. Key features and topics: * balanced presentation geared toward undergraduate and beginning statistics students not yet specializing in a specific area of application * extensive exercises at all levels of difficulty employing well-known computing packages such as SAS and SYSTAT * real-world applications to the health sciences, manufacturing and industrial processes * thorough explanations of methods and tests moving away from the "cookbook” approach commonly used in other textbooks * coverage of topics not found in textbooks at the same level: current status data, step stress tests, and threshold regression * solutions manual available to instructors adopting the text. An Introduction to Lifetime Data Analysis may be used as a textbook for undergraduate and beginning-level graduate courses in statistics. Prerequisites include distribution theory and maximum likelihood-based statistical inference, as well as familiarity with standard elementary methods of analysis up to regression. The text is also an excellent reference for advanced students, researchers, and practitioners in applied statistics.
The analysis of failure and lifetime data plays a major role in modern statistics. Typical examples include the analysis of the time and load at which a machine fails, as well as the analysis of the time it takes for a patient either to die or to reach a milestone in his or her illness or recovery. The former is an example of Reliabilty Analysis, common in engineering and physical sciences, while the latter is found in biomedical studies and falls under Survival Analysis category. This volume covers all areas pertaining to both Reliability Analysis and Survival Analysis. An Introduction to Lifetime Data Analysis may be used as a textbook for advanced undergraduate and graduate courses in statistics. Prerequisites include distribution theory and maximum likelihood based statistical inference, plus familiarity with standard elementary methods of analysis up to regression. The text is also an excellent reference for students, researchers, and practitioners in applied statistics.

Autor*in

N. Balakrishnan

Themen in »An Introduction to Lifetime Data Analysis«

Cox's proportional hazards model accelerated life censoring covariance current status data failure analysis lifetime data nonnegative random variables parametric models proportional hazards regression recurrent events reliability analysis semi-parametric models step stress tests survival analysis

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