Modeling Survival Data Using Frailty Models

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.

“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)

• 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.