Advanced survival models [electronic resource] / Catherine Legrand.

By:

Legrand, Catherine [author.]

Material type: Text

TextSeries: Publication details: FL : CRC Press, c2021.Edition: First editionDescription: 1 online resource (xxviii, 332 pages) : illustrations (black and white)ISBN:

9780429054167
0429054165
9780429620409
0429620403

Subject(s):

Medicine -- Research -- Statistical methods

DDC classification:

610.727 LEG

Contents:

1. Introduction 2. Classical Survival Analysis 3. Frailty Models 4. Cure Models 5. Competing Risks 6. Joint Modeling

Summary: 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.

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
General Books	CUTN Central Library Medicine, Technology & Management	Non-fiction	610.727 LEG (Browse shelf(Opens below))	Available		47485

"A Chapman & Hall book"

1. Introduction
2. Classical Survival Analysis
3. Frailty Models
4. Cure Models
5. Competing Risks
6. Joint Modeling

Online version restricted to NUS staff and students only through NUSNET.

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.

Mode of access: World Wide Web.

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