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Modeling Survival Data Using Frailty Models

Second Edition

  • Book
  • © 2019

Overview

Authors:
  1. David D. Hanagal

    1. Symbiosis Statistical Institute, Symbiosis International University, Pune, India

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  • 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

Part of the book series: Industrial and Applied Mathematics (INAMA)

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Table of contents (14 chapters)

  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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Keywords

About this book

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.

Reviews

“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)

Authors and Affiliations

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

    David D. Hanagal

About the author

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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