Robert Elashoff, Gang Li and Ning Li. Joint Modeling of Longitudinal and Time‐to‐Event Data. Boca Raton, CRC Press.

Robert Elashoff, Gang Li and Ning Li. Joint Modeling of Longitudinal and Time‐to‐Event Data.... This book is a comprehensive state‐of‐the‐art treatment of joint models for time‐to‐event and longitudinal data with numerous applications to real‐world problems. Chapter 1 describes 11 data sets that are used as illustration throughout the book. Chapter 2 is an introduction to standard methods for longitudinal data that begins with a reminder of Rubin's classification of missingness mechanisms. It follows with brief presentations of linear and generalized linear mixed models, and generalized estimating equations and their weighting counterparts for missing data, before terminating with a description of multiple imputation. These methods are illustrated by quite complex analyses of several data sets.Chapter 3 describes methods for survival data analyses with special emphasis on accelerated failure time and competing risk models. Several small examples presenting special cases of these models help the reader to understand their interpretations and interrelationships, but surprisingly, this chapter does not include a real data analysis.Chapter 4 is the core chapter of the book that introduces joint models for one longitudinal marker and one time‐to‐event outcome by successively describing numerous published joint models. This chapter is quite different from other textbook or review articles on joint models as the authors focus mainly on the non‐ignorable missing data problem. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Wiley

Robert Elashoff, Gang Li and Ning Li. Joint Modeling of Longitudinal and Time‐to‐Event Data. Boca Raton, CRC Press.

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
© 2018, The International Biometric Society
ISSN
0006-341X
eISSN
1541-0420
D.O.I.
10.1111/biom.12850
Publisher site
See Article on Publisher Site

Abstract

This book is a comprehensive state‐of‐the‐art treatment of joint models for time‐to‐event and longitudinal data with numerous applications to real‐world problems. Chapter 1 describes 11 data sets that are used as illustration throughout the book. Chapter 2 is an introduction to standard methods for longitudinal data that begins with a reminder of Rubin's classification of missingness mechanisms. It follows with brief presentations of linear and generalized linear mixed models, and generalized estimating equations and their weighting counterparts for missing data, before terminating with a description of multiple imputation. These methods are illustrated by quite complex analyses of several data sets.Chapter 3 describes methods for survival data analyses with special emphasis on accelerated failure time and competing risk models. Several small examples presenting special cases of these models help the reader to understand their interpretations and interrelationships, but surprisingly, this chapter does not include a real data analysis.Chapter 4 is the core chapter of the book that introduces joint models for one longitudinal marker and one time‐to‐event outcome by successively describing numerous published joint models. This chapter is quite different from other textbook or review articles on joint models as the authors focus mainly on the non‐ignorable missing data problem.

Journal

BiometricsWiley

Published: Jan 1, 2018

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