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Modeling Survival Data Using Frailty Models: Second Edition de David D. Hanagal

Modeling Survival Data Using Frailty Models: Second Edition: Industrial and Applied Mathematics

Autor David D. Hanagal
en Limba Engleză Paperback – 31 ian 2021
This book presents the basic concepts of survival analysis and frailty models, covering both fundamental and advanced topics. It focuses on applications of statistical tools in biology and medicine, highlighting the latest frailty-model methodologies and applications in these areas. After explaining the basic concepts of survival analysis, the book goes on to discuss shared, bivariate, and correlated frailty models and their applications. It also features nine datasets that have been analyzed using the R statistical package. Covering recent topics, not addressed elsewhere in the literature, this book is of immense use to scientists, researchers, students and teachers.
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Specificații

ISBN-13: 9789811511837
ISBN-10: 9811511837
Pagini: 295
Ilustrații: XXIV, 295 p. 50 illus., 15 illus. in color.
Dimensiuni: 155 x 235 mm
Ediția:1st ed. 2019
Editura: Springer Nature Singapore
Colecția Springer
Seria Industrial and Applied Mathematics

Locul publicării:Singapore, Singapore

Cuprins

1 Introduction to Survival Analysis.- 2 Some Parametric Models.- 3 Nonparametric and Semiparametric Models.- 4 The Frailty Concept.- 5 Various Frailty Models.- 6 Estimation Methods for Shared Frailty Models.- 7 Analysis of Survival Data in Shared Frailty Models.- 8 Tests of Hypotheses in Frailty Models.- 9 Shared Gamma Frailty Models.- 10 Shared Gamma Frailty Models Based on Reversed Hazard.- 11 Bivariate Frailty Models and Estimation Methods.- 12 Correlated Frailty Models.- 13 Correlated Gamma and Inverse Gaussian Frailty Models.- 14 Correlated Gamma Frailty Models Based on Reversed Hazard.

Recenzii

“This is an excellent book on survival models suitable for undergraduate students and physical and medical scientists who have a background on probability theory.” (Nirode C. Mohanty, zbMATH 1459.62003, 2021)

Notă biografică

David D. Hanagal is an Honorary Professor at the Symbiosis Statistical Institute, Symbiosis International University, Pune, India. He was previously a professor at the Department of Statistics, Savitribai Phule Pune University, India. An elected fellow of the Royal Statistical Society, UK, he is an editor and on the editorial board of several respected international journals. He has authored two books and published over 125 research publications in leading journals. With 30 years of research experience, he is an expert on writing programs using SAS, R, MATLAB, MINITAB, SPSS, and SPLUS. He also has worked as a visiting professor at several universities in the USA, Germany, and Mexico, and delivered a number of talks at conferences around the globe. His research interests include statistical inference, selection problems, reliability, survival analysis, frailty models, Bayesian inference, stress–strength models, Monte Carlo methods, MCMC algorithms, bootstrapping, censoring schemes, distribution theory, multivariate models, characterizations, repair and replacement models, software reliability, quality loss index, and nonparametric inference.

Textul de pe ultima copertă

This book presents the basic concepts of survival analysis and frailty models, covering both fundamental and advanced topics. It focuses on applications of statistical tools in biology and medicine, highlighting the latest frailty-model methodologies and applications in these areas. After explaining the basic concepts of survival analysis, the book goes on to discuss shared, bivariate, and correlated frailty models and their applications. It also features nine datasets that have been analyzed using the R statistical package. Covering recent topics, not addressed elsewhere in the literature, this book is of immense use to scientists, researchers, students and teachers.

Caracteristici

Discusses fundamental as well as advanced concepts of frailty models Covers frailty models for survival data with recent methodology and applications Analyzes datasets using the R statistical package, which is free and open access