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Modeling discrete time-to-event data

Schmid, Matthias.

Modeling discrete time-to-event data

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modeling discrete time-to-event databy Gerhard Tutz, Matthias Schmid.
Author:	Tutz, Gerhard.
other author:	Schmid, Matthias.
Published:	Cham :Springer International Publishing :2016.
Description:	x, 247 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Discrete-time systemsMathematical models.
Online resource:	http://dx.doi.org/10.1007/978-3-319-28158-2
ISBN:	9783319281582$q(electronic bk.)

Modeling discrete time-to-event data
Tutz, Gerhard.

Modeling discrete time-to-event data[electronic resource] /by Gerhard Tutz, Matthias Schmid. - Cham :Springer International Publishing :2016. - x, 247 p. :ill. (some col.), digital ;24 cm. - Springer series in statistics,0172-7397. - Springer series in statistics..

Introduction -- The Life Table -- Basic Regression Models -- Evaluation and Model Choice -- Nonparametric Modelling and Smooth Effects -- Tree-Based Approaches -- High-Dimensional Models - Structuring and Selection of Predictors -- Competing Risks Models -- Multiple-Spell Analysis -- Frailty Models and Heterogeneity -- Multiple-Spell Analysis -- List of Examples -- Bibliography -- Subject Index -- Author Index.

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.

ISBN: 9783319281582$q(electronic bk.)

Standard No.: 10.1007/978-3-319-28158-2doiSubjects--Topical Terms:

359884
Discrete-time systems
--Mathematical models.

LC Class. No.: QA402

Dewey Class. No.: 003.83

Modeling discrete time-to-event data
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245 1 0 $a Modeling discrete time-to-event data $h [electronic resource] / $c by Gerhard Tutz, Matthias Schmid.
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490 1 $a Springer series in statistics, $x 0172-7397
505 0 $a Introduction -- The Life Table -- Basic Regression Models -- Evaluation and Model Choice -- Nonparametric Modelling and Smooth Effects -- Tree-Based Approaches -- High-Dimensional Models - Structuring and Selection of Predictors -- Competing Risks Models -- Multiple-Spell Analysis -- Frailty Models and Heterogeneity -- Multiple-Spell Analysis -- List of Examples -- Bibliography -- Subject Index -- Author Index.
520 $a 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.
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