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  • Book
  • © 2016

Modeling Discrete Time-to-Event Data

  • Provides the first comprehensive overview of statistical methods for discrete failure times

  • Contains numerous examples and exercises that illustrate the presented methods

  • Introduces novel methodology for model selection, nonparametric estimation and model evaluation that is new in the context of discrete failure analysis

  • Reproducible data through freely available R codes

Part of the book series: Springer Series in Statistics (SSS)

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eBook USD 84.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-28158-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
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  • Tax calculation will be finalised during checkout
Softcover Book USD 109.99
Price excludes VAT (USA)
Hardcover Book USD 109.99
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Table of contents (10 chapters)

  1. Front Matter

    Pages i-x
  2. Introduction

    • Gerhard Tutz, Matthias Schmid
    Pages 1-13
  3. The Life Table

    • Gerhard Tutz, Matthias Schmid
    Pages 15-34
  4. Basic Regression Models

    • Gerhard Tutz, Matthias Schmid
    Pages 35-72
  5. Evaluation and Model Choice

    • Gerhard Tutz, Matthias Schmid
    Pages 73-104
  6. Nonparametric Modeling and Smooth Effects

    • Gerhard Tutz, Matthias Schmid
    Pages 105-127
  7. Tree-Based Approaches

    • Gerhard Tutz, Matthias Schmid
    Pages 129-148
  8. High-Dimensional Models: Structuring and Selection of Predictors

    • Gerhard Tutz, Matthias Schmid
    Pages 149-165
  9. Competing Risks Models

    • Gerhard Tutz, Matthias Schmid
    Pages 167-184
  10. Frailty Models and Heterogeneity

    • Gerhard Tutz, Matthias Schmid
    Pages 185-211
  11. Multiple-Spell Analysis

    • Gerhard Tutz, Matthias Schmid
    Pages 213-223
  12. Back Matter

    Pages 225-247

About this book

This book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because they have been measured in intervals (e.g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are explained. Each section includes a set of exercises on the respective topics. Various functions and tools for the analysis of discrete survival data are collected in the R package discSurv that accompanies the book. 

Keywords

  • Survival data
  • Survival functions
  • Discrete hazard function
  • Time-to-Event Data
  • Life tables
  • Discrete hazard model
  • Continuation ratio model
  • Goodness-of-Fit
  • Time-dependent AUC
  • discSurv
  • Interval censoring
  • Recursive partitioning
  • Multiple spells
  • Competing risks
  • Generalized estimation equations
  • Sequential methods in item response theory
  • Discrete frailty model
  • Smooth effects
  • Additive models
  • Penalized regression
  • Gradient boosting

Reviews

“Modeling Discrete Time-to-Event Data provides an excellent overview of a field that is underrepresented in the literature. At what it aims to do, striking a balance between theory and practice, this book does a great job. Its readers will understand not only what to do, but also how to do it. I believe that this book can easily find a place on the shelf of statisticians who have an interest in survival analysis.” (Theodor Adrian Balan, Biometrical Journal, Vol. 61 (1), January, 2019)​

Authors and Affiliations

  • LMU Munich, München, Germany

    Gerhard Tutz

  • University of Bonn, Bonn, Germany

    Matthias Schmid

About the authors

Gerhard Tutz is a professor of statistics at the Department of Statistics at the University of Munich. He has published several books with Springer.
Matthias Schmid is a professor of Medical Biometry, Informatics and Epidemiology at the University of Bonn. He received his diploma (2004) and his Ph.D. (2007) in statistics at the University of Munich and his habilitation (2012) in biostatistics at the University of Erlangen. Before working in Bonn, he was professor of computational statistics at the Department of Statistics at the University of Munich (2013-2014).

Bibliographic Information

Buying options

eBook USD 84.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-28158-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 109.99
Price excludes VAT (USA)
Hardcover Book USD 109.99
Price excludes VAT (USA)