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Competing risks and multistate models with R / Jan Beyersmann, Arthur Allignol, Martin Schumacher.

By: Beyersmann, Jan.
Contributor(s): Allignol, Arthur | Schumacher, Martin, 1950-.
Material type: TextTextSeries: Use R!: Publisher: New York : Springer, c2012Description: xi, 245 p. : ill. ; 24 cm.ISBN: 9781461420347 (pbk. : alk. paper); 1461420342 (pbk. : alk. paper); 9781461420354 (eISBN).Subject(s): Statistics -- Computer programs | R (Computer program language)DDC classification: 519.502 Online resources: WorldCat details | E-book Fulltext
Contents:
Table of contents Data examples.- An informal introduction to hazard-based analyses.- Competing risks.- Multistate modelling of competing risks.- Nonparametric estimation.- Proportional hazards models.- Nonparametric hypothesis testing.- Further topics in competing risks.- Multistate models and their connection to competing risks.- Nonparametric estimation.- Proportional transition hazards models.- Time-dependent covariates and multistate models.- Further topics in multistate modeling.
Summary: This book explains hazard-based analyses of competing risks and multistate data using the R statistical programming code, placing special emphasis on interpretation of results. Includes real data examples, and encourages readers to simulate their own data.
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Text Text EWU Library
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Includes bibliographical references (p. 233-240) and index.

Table of contents Data examples.- An informal introduction to hazard-based analyses.- Competing risks.- Multistate modelling of competing risks.- Nonparametric estimation.- Proportional hazards models.- Nonparametric hypothesis testing.- Further topics in competing risks.- Multistate models and their connection to competing risks.- Nonparametric estimation.- Proportional transition hazards models.- Time-dependent covariates and multistate models.- Further topics in multistate modeling.

This book explains hazard-based analyses of competing risks and multistate data using the R statistical programming code, placing special emphasis on interpretation of results. Includes real data examples, and encourages readers to simulate their own data.

Applied Statistics

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