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 and complements the books on right-censored data.

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.

Is dedicated to models and methods specifically developed for interval-censored failure time data

Autorentext

Jianguo (Tony) Sun is a professor at the Department of Statistics of the University of Missouri. He obtained his Ph.D. at the University of Waterloo and has been developing novel statistical methods for the analysis of interval-censored failure time data and panel count data over the last 20 years. In particular, he published "The Statistical Analysis of Interval-censored Failure Time Data" (Springer, 2006), the first book on interval-censored data. He also co-authored with Drs. Chen and Peace the volume "Interval-censored Time-to-Event Data: Methods and Applications" (Chapman and Hall, 2012). Xingqiu Zhao is a faculty member of The Hong Kong Polytechnic University and she obtained her Ph.D. at McMaster University. Her research interests include econometrics, financial mathematics, longitudinal data analysis, stochastic process models and applications, survival analysis, and time series analysis. In particular, she has published many papers on new statistical inference procedures for analyzing interval-censored failure time data, recurrent event data and panel count data.

Inhalt
Inference for Parametric Models and Imputation Approaches.- Nonparametric Maximum Likelihood Estimation.- Comparison of Survival Functions.- Regression Analysis of Current Status Data.- Regression Analysis of Case II Interval-censored Data.- Analysis of Bivariate Interval-censored Data.- Analysis of Doubly Censored Data.- Analysis of Panel Count Data.- Other Topics.