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Survival analysis, the analysis of failure time data, is a rapid developing area and a number of books on the topic have been published in last twenty-five years. However, all of these books deal with right-censored failure time data, not the analysis of interval-censored failure time data. Interval-censored data include right-censored data as a special case and occur in many fields. The analysis of interval-censored data is much more difficult than that of right-censored data because the censoring mechanism that yields interval censoring is more complicated than that for right censoring.
This book collects and unifies statistical models and methods that have been proposed for analyzing interval-censored failure time data. It provides the first comprehensive coverage of the topic of interval-censored data and complements the books on right-censored data. A number of inference approaches are discussed in the book, including the maximum likelihood, estimating equations, sieve maximum likelihood, and conditional likelihood. One major difference between the analyses of right- and interval-censored data is that the theory of counting processes, which is responsible for substantial advances in the theory and development of modern statistical methods for right-censored data, is not applicable to interval-censored data. The focus of the book is on nonparametric and semiparametric inferences, but it also describes parametric and imputation approaches. In addition, Bayesian methods and the analysis of interval-censored data with informative interval censoring are considered as well as the analysis of interval-censored recurrent event, or panel count, data.
This book provides an up-to-date reference for people who are conducting research on the analysis of interval-censored failure time data as well as for those who need to analyze interval-censored data to answer substantive questions. It can also be used as a text for a graduate course in statistics or biostatistics that assume a basic knowledge of probability and statistics.
Jianguo (Tony) Sun is a professor at the Department of Statistics of the University of Missouri-Columbia. He has developed novel statistical methods for the analysis of interval-censored failure time data and panel count data over the last fifteen years.
This book collects and unifies statistical models and methods that have been proposed for analyzing interval-censored failure time data. It provides the first comprehensive coverage of the topic of interval-censored data and complements the books on right-censored data. A number of inference approaches are discussed in the book, including the maximum likelihood, estimating equations, sieve maximum likelihood, and conditional likelihood. One major difference between the analyses of right- and interval-censored data is that the theory of counting processes, which is responsible for substantial advances in the theory and development of modern statistical methods for right-censored data, is not applicable to interval-censored data. The focus of the book is on nonparametric and semiparametric inferences, but it also describes parametric and imputation approaches. In addition, Bayesian methods and the analysis of interval-censored data with informative interval censoring are considered as well as the analysis of interval-censored recurrent event, or panel count, data.
This book provides an up-to-date reference for people who are conducting research on the analysis of interval-censored failure time data as well as for those who need to analyze interval-censored data to answer substantive questions. It can also be used as a text for a graduate course in statistics or biostatistics that assume a basic knowledge of probability and statistics.
Jianguo (Tony) Sun is a professor at the Department of Statistics of the University of Missouri-Columbia. He has developed novel statistical methods for the analysis of interval-censored failure time data and panel count data over the last fifteen years.
From the reviews: "Interval-censored data, where one does not observe the exact times of events of interest ... but only that they occur in certain intervals ... . This is the first book to summarize the substantial body of recent work in this field. ... The book includes a number of applications, with full sets of data provided. Algorithms for fitting the various models presented are described carefully in the text and there is a short section discussing available statistical software ... ." (D. Oakes, Short Book Reviews, Vol. 26 (3), 2006) "...[T]this book is to be recommended to scientists in the field of survival analysis, and in particular to those researchers in interval-censored data, as an up-to-date reference in frequentist approaches...the book might be also useful to practitioners from other disciplines who can find in this text the suitable methods to analyze interval-censored data." (Robert G. McLeod frorm Biometrics, Issue 63, September 2007) "This is the first book that provides a comprehensive summary of important topics in the field; therefore, there is no doubt that this book represents an important contribution to the literature. ... In summary, this book is good news for those interested in an overview of interval-censored data. ... This book offers a well-written and comprehensive summary. I enjoyed reading it and feel that it provides an excellent guide to this important area. It will definitely be kept on my shelf for reference." (Do-Hwan Park, Journal of the American Statistical Association, Vol. 102 (480), 2007) "This book is a survey of statistical analysis tools for interval-censored data which occur rather frequently in various empirical sciences, e.g., in demography, epidemiology, finance, medicine or sociology. ... This volume is adequate for self-study for researchers and students who want to handle and analyze interval-censored data." (Christina Wunder, Zentralblatt MATH, Vol. 1127 (4), 2008)