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Advanced Survival Models

Author

Listed:
  • Legrand, Catherine

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

Abstract

Survival data analysis is a very broad field of statistics, encompassing a large variety of methods used in a wide range of applications, and in particular in medical research. During the last twenty years, several extensions of "classical" survival models have been developed to address particular situations often encountered in practice. This book aims to gather in a single reference the most commonly used extensions, such as frailty models (in case of unobserved heterogeneity or clustered data), cure models (when a fraction of the population will not experience the event of interest), competing risk models (in case of different types of event), and joint survival models for a time-to-event endpoint and a longitudinal outcome. Features Presents state-of-the art approaches for different advanced survival models including frailty models, cure models, competing risk models and joint models for a longitudinal and a survival outcome Uses consistent notation throughout the book for the different techniques presented Explains in which situation each of these models should be used, and how they are linked to specific research questions Focuses on the understanding of the models, their implementation, and their interpretation, with an appropriate level of methodological development for masters students and applied statisticians Provides references to existing R packages and SAS procedure or macros, and illustrates the use of the main ones on real datasets This book is primarily aimed at applied statisticians and graduate students of statistics and biostatistics. It can also serve as an introductory reference for methodological researchers interested in the main extensions of classical survival analysis.

Suggested Citation

  • Legrand, Catherine, 2021. "Advanced Survival Models," LIDAM Reprints ISBA 2021015, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  • Handle: RePEc:aiz:louvar:2021015
    DOI: https://doi.org/10.1201/9780429054167
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    Cited by:

    1. Linden, Mikael, 2024. "Optimal Retirement Age: Death Hazard Rate Approach," MPRA Paper 120786, University Library of Munich, Germany.
    2. Janette Larney & James Samuel Allison & Gerrit Lodewicus Grobler & Marius Smuts, 2023. "Modelling the Time to Write-Off of Non-Performing Loans Using a Promotion Time Cure Model with Parametric Frailty," Mathematics, MDPI, vol. 11(10), pages 1-17, May.
    3. Adam Braima S. Mastor & Abdulaziz S. Alghamdi & Oscar Ngesa & Joseph Mung’atu & Christophe Chesneau & Ahmed Z. Afify, 2023. "The Extended Exponential-Weibull Accelerated Failure Time Model with Application to Sudan COVID-19 Data," Mathematics, MDPI, vol. 11(2), pages 1-26, January.
    4. Jolien Ponnet & Robin Van Oirbeek & Tim Verdonck, 2021. "Concordance Probability for Insurance Pricing Models," Risks, MDPI, vol. 9(10), pages 1-26, October.
    5. Tshilidzi Benedicta Mulaudzi & Yehenew Getachew Kifle & Roel Braekers, 2023. "A Shared Frailty Model for Left-Truncated and Right-Censored Under-Five Child Mortality Data in South Africa," Stats, MDPI, vol. 6(4), pages 1-11, October.

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