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.
Table des matières
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.
A propos de l’auteur
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.
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