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Modeling discrete time-to-event data / Gerhard Tutz and Matthias Schmid.

By: Contributor(s): Material type: TextTextSeries: Springer series in statisticsPublication details: Switzerland : Springer, 2016.Description: x, 247 pages : illustrations ; 24 cmISBN:
  • 9783319281568
Subject(s): DDC classification:
  • 000SB:003.83 23 T967
Contents:
1. Introduction -- 2. The Life Table -- 3. Basic Regression Models -- 4. Evaluation and Model Choice -- 5. Nonparametric Modelling and Smooth Effects -- 6. Tree-Based Approaches -- 7. High-Dimensional Models: Structuring and Selection of Predictors -- 8. Competing Risks Models -- 9. Frailty Models and Heterogeneity -- 10. Multiple-Spell Analysis -- List of Examples -- Bibliography --
Summary: 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.
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Includes bibliographical references and indexes.

1. Introduction --
2. The Life Table --
3. Basic Regression Models --
4. Evaluation and Model Choice --
5. Nonparametric Modelling and Smooth Effects --
6. Tree-Based Approaches --
7. High-Dimensional Models: Structuring and Selection of Predictors --
8. Competing Risks Models --
9. Frailty Models and Heterogeneity --
10. Multiple-Spell Analysis --
List of Examples --
Bibliography --

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.

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