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Statistical Inference on Residual Life de Jong-Hyeon Jeong

Statistical Inference on Residual Life: Statistics for Biology and Health

Autor Jong-Hyeon Jeong
en Limba Engleză Hardback – 21 ian 2014
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.
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Specificații

ISBN-13: 9781493900046
ISBN-10: 1493900048
Pagini: 216
Ilustrații: XI, 201 p. 17 illus., 12 illus. in color.
Dimensiuni: 160 x 241 x 18 mm
Greutate: 0.44 kg
Ediția:2014
Editura: Springer
Colecția Statistics for Biology and Health
Seria Statistics for Biology and Health

Locul publicării:New York, NY, United States

Public țintă

Research

Cuprins

Introduction.- Inference on Mean Residual Life.- Quantile Residual Life.- Quantile Residual Life under Competing Risks.- Other Methods for Inference on Quantiles.- Study Design based on Quantile (Residual) Life.- Appendix: R codes.- References.- Index.

Recenzii

“It is the very first book in its kind that is entirely devoted to the statistical methodologies aimed to analyze residual life and related quantities. … would be a must-have item for researchers who are interested in learning statistical theory on the quantile residual life functions. … would be a valuable asset to those who work on survival analysis. It would be beneficial to a wide group of audience who are interested in the analysis of quantile residual functions.” (Sangwook Kang, Journal of Agricultural, Biological, and Environmental Statistics, Vol. 20, 2015)
“This book on the statistical analysis of life expectancy focuses on history, research achievements, and recent developments in statistical inference on quantile residual lifetime. … 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. … This book is strongly recommended to beginning researchers and statistician who are interested in learning the theory and application of the residual life function.” (Samit Bhatheja, Doody’s Book Reviews, May, 2014)

Notă biografică

Il Do Ha is a full professor in the Department of Statistics at Pukyong National University in South Korea. His research interests are multivariate survival analysis using h-likelihood, inferences on random-effect models, clinical trials and financial statistics. Dr. Ha received his Ph.D. degree in statistics from Seoul National University. He has served as an Associate Editor of Computational Statistics until 2008-2012 and has been a fellow of the Royal Statistical Society (RSS) since 2006. Jong-Hyeon Jeong is a full professor in the Department of Biostatistics at University of Pittsburgh in USA. His research interests are in survival analysis, including competing risks, quantile residual life, empirical likelihood, h-likelihood, frailty model and clinical trials. He has published his first book with Springer: Jeong, J.-H. (2014) Statistical Inference on Residual Life, New York: Springer. Dr. Jeong received his Ph.D. degree in statistics from University of Rochester. He has been a fellow of the American Statistical Association (ASA) since 2017 as well as an elected member of the international Statistical Institute (ISI) since 2007. Dr. Jeong is also serving on the editorial board for the journal "Lifetime Data Analysis". Youngjo Lee is a full professor in the Department of Statistics at Seoul National University in South Korea and also an adjunct professor of Karolinska Institutet in Sweden. His research interests are extension, application, theory and software development for hierarchical GLM (HGLM) and multivariate survival models using h-likelihood. He has published a HGLM book with Chapman and Hall: Lee, Y., Nelder, J. A. and Pawitan, Y. (2017) Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood, 2nd edition, Boca Raton: Chapman and Hall. Dr. Lee received his Ph.D. degree in statistics from Iowa State University. He has been a fellow of the Royal Statistical Society (RSS) since 1996 as well as the American Statistical Association (ASA) since 2013.

Textul de pe ultima copertă

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.

Caracteristici

Extensively reviews statistical inference methods on the mean residual lifetime Covers various aspects of frequentist and Bayesian methods for the quantile residual life function in survival analysis and reliability theory Presents new statistical methods to design based on the residual life distribution Includes supplementary material: sn.pub/extras