Statistical Analysis of Panel Count Data (eBook)

Panel count data occur in studies that concern recurrent events, or event history studies, when study subjects are observed only at discrete time points.  By recurrent events, we mean the event that can occur or happen multiple times or repeatedly. Examples of recurrent events include disease infections, hospitalizations in medical studies, warranty claims of automobiles or system break-downs in reliability studies.  In fact, many other fields yield event history data too such as demographic studies, economic studies and social sciences.  For the cases where the study subjects are observed continuously, the resulting data  are usually referred to as recurrent event data.

This book collects and unifies statistical models and methods that have been developed for analyzing panel count data.  It provides the first comprehensive coverage of the topic. The main focus is on methodology, but for the benefit of the reader, the applications of the methods to real data are also discussed along with numerical calculations. There exists a great deal of literature on the analysis of recurrent event data. This book fills the void in the literature on the analysis of panel count data.

This book provides an up-to-date reference for scientists who are conducting research on the analysis of panel count data. It will also be instructional for those who need to analyze panel count data to answer substantive research questions.  In addition, it can be used as a text for a graduate course in statistics or biostatistics that assumes a basic knowledge of probability and statistics. 

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.

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Panel count data occur in studies that concern recurrent events, or event history studies, when study subjects are observed only at discrete time points. By recurrent events, we mean the event that can occur or happen multiple times or repeatedly. Examples of recurrent events include disease infections, hospitalizations in medical studies, warranty claims of automobiles or system break-downs in reliability studies. In fact, many other fields yield event history data too such as demographic studies, economic studies and social sciences. For the cases where the study subjects are observed continuously, the resulting data are usually referred to as recurrent event data.This book collects and unifies statistical models and methods that have been developed for analyzing panel count data. It provides the first comprehensive coverage of the topic. The main focus is on methodology, but for the benefit of the reader, the applications of the methods to real data are also discussed along with numerical calculations. There exists a great deal of literature on the analysis of recurrent event data. This book fills the void in the literature on the analysis of panel count data.This book provides an up-to-date reference for scientists who are conducting research on the analysis of panel count data. It will also be instructional for those who need to analyze panel count data to answer substantive research questions. In addition, it can be used as a text for a graduate course in statistics or biostatistics that assumes a basic knowledge of probability and statistics.