Modeling Survival Data Using Frailty Models

1. Front Matter

   Pages i-xxiv

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2. Basic Concepts in Survival Analysis

   1. Front Matter

      Pages 1-1

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   2. Introduction to Survival Analysis

      • David D. Hanagal

      Pages 3-18

   3. Some Parametric Models

      • David D. Hanagal

      Pages 19-47

   4. Nonparametric and Semiparametric Models

      • David D. Hanagal

      Pages 49-70

3. Shared Frailty Models for Survival Data

   1. Front Matter

      Pages 71-71

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   2. The Frailty Concept

      • David D. Hanagal

      Pages 73-83

   3. Various Frailty Models

      • David D. Hanagal

      Pages 85-121

   4. Estimation Methods for Shared Frailty Models

      • David D. Hanagal

      Pages 123-134

   5. Analysis of Survival Data in Shared Frailty Models

      • David D. Hanagal

      Pages 135-151

   6. Tests of Hypotheses in Frailty Models

      • David D. Hanagal

      Pages 153-163

   7. Shared Gamma Frailty Models

      • David D. Hanagal

      Pages 165-190

   8. Shared Gamma Frailty Models Based on Reversed Hazard

      • David D. Hanagal

      Pages 191-211

   9. Comparison of Gamma and Inverse Gaussian Frailty Models

      • David D. Hanagal

      Pages 213-248

4. Correlated Frailty Models for Survival Data

   1. Front Matter

      Pages 249-249

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   2. Correlated Frailty Models

      • David D. Hanagal

      Pages 251-268

   3. Correlated Gamma and Inverse Gaussian Frailty Models

      • David D. Hanagal

      Pages 269-278

   4. Correlated Frailty Models Based on Reversed Hazard

      • David D. Hanagal

      Pages 279-292

5. Back Matter

   Pages 293-295

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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.

• Survival Analysis
• Frailty Models
• Bayesian Inference
• Statistical Modelling
• Parametric Models
• Non-parametric Models
• Semi-parametric Models
• Estimation Methods
• Hypothesis Testing

“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)

• Symbiosis Statistical Institute, Symbiosis International University, Pune, India

  David D. Hanagal

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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