×

Uh-oh, it looks like your Internet Explorer is out of date.

For a better shopping experience, please upgrade now.

Statistical Inference on Residual Life
     

Statistical Inference on Residual Life

by Jong-Hyeon Jeong
 

See All Formats & Editions

This is a monograph on the concept of residual life, which is an alternative summary measure of time-to-event data, or survival data. The mean residual life has been used for many years under the name of life expectancy, so it is a natural concept for summarizing survival or reliability data. It is also more interpretable than the popular hazard function,

Overview

This is a monograph on the concept of residual life, which is an alternative summary measure of time-to-event data, or survival data. The mean residual life has been used for many years under the name of life expectancy, so it is a natural concept for summarizing survival or reliability data. It is also more interpretable than the popular hazard function, especially for communications between patients and physicians regarding the efficacy of a new drug in the medical field. This book reviews existing statistical methods to infer the residual life distribution. The review and comparison includes existing inference methods for mean and median, or quantile, residual life analysis through medical data examples. The concept of the residual life is also extended to competing risks analysis. The targeted audience includes biostatisticians, graduate students, and PhD (bio)statisticians. Knowledge in survival analysis at an introductory graduate level is advisable prior to reading this book.

Editorial Reviews

Doody's Review Service
Reviewer: Samit Bhatheja, MD, MPH (East Tennessee State University Quillen College of Medicine)
Description: This book on the statistical analysis of life expectancy focuses on history, research achievements, and recent developments in statistical inference on quantile residual lifetime.
Purpose: The purpose is to introduce the basic concepts needed to investigate the properties of the quantile (residual life) as well as provide an overview of statistical methods developed to infer the mean residual life. This book will help users in learning the theory and application of the quantile (residual life) function and provide future research directions.
Audience: The intended audience includes graduate students and researchers both in academia and in industry who are interested in learning the theory and application of the residual life function. The author is an elected member of the International Statistical Institute and has research interests in survival analysis and clinical trials.
Features: The first of the book's chapters describes the basic concepts needed to investigate properties of residual life function, before focusing on the various statistical methods developed to infer the mean residual life function in chapter 2. Chapter 3 further describes methods which have been recently developed to infer the mean residual life. Chapter 4 further elaborates on the results in the previous chapter. Chapter 5 focuses on alternative approaches based on empiric likelihood and chapter 6 teaches study design based on quantile (residual life). The author uses numerical examples and sample cases with small datasets to help readers quickly grasp the concepts and learn application of the concepts.
Assessment: This book does an excellent job of presenting the basic concepts of residual life with various statistical methods, including recently developed methods, to infer the mean residual life as well as alternatives to this approach with easy-to-understand numerical examples and sample cases. This book is strongly recommended to beginning researchers and statistician who are interested in learning the theory and application of the residual life function.

Product Details

ISBN-13:
9781493900046
Publisher:
Springer New York
Publication date:
02/28/2014
Series:
Statistics for Biology and Health Series
Edition description:
2014
Pages:
201
Product dimensions:
6.10(w) x 9.25(h) x 0.03(d)

Meet the Author

Dr. Jong-Hyeon Jeong is a full professor of Biostatistics at the University of Pittsburgh. Dr. Jeong's main research area has been survival analysis and clinical trials. In survival analysis, he has worked on frailty modeling, efficiency of survival probability estimates from the proportional hazards model, weighted log-rank test, competing risks, quantile residual life, and likelihood theory such as empirical likelihood and hierarchical likelihood. In clinical trials, he has been involved in several phase III clinical trials on breast cancer treatment as the primary statistician. He has been teaching statistical theory courses and survival analysis in the Department of Biostatistics at the University of Pittsburgh. Dr. Jeong holds his PhD degree in statistics from the University of Rochester and has been an elected member of the International Statistical Institute (ISI) since 2007.

Customer Reviews

Average Review:

Post to your social network

     

Most Helpful Customer Reviews

See all customer reviews