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Introduction and ExamplesIntroduction
Methods for Ignorable Missing Data
Introduction
Missing Data Mechanisms
Linear and Generalized Linear Mixed Models
Generalized Estimating Equations
Fruther topics
Time-to-event data analysis
Right censoring
Survival function and hazard function
Estimation of a survival function
Cox's semiparametric multiplicative hazards models
Accelerated failure time models with time-independent covariates
Accelerated failure time model with time-dependent covariates
Methods for competing risks data
Further topics
Overview of Joint Models for Longitudinal and Time-to-Event Data
Joint Models of Longitudinal Data and an Event time
Joint Models with Discrete Event Times and Monotone Missingness
Longitudinal Data with Both Monotone and Intermittent Missing Values
Event Time Models with Intermittently Measured Time Dependent Covariates
Longitudinal Data with Informative Observation Times
Dynamic Prediction in Joint Models
Joint Models for Longitudinal Data and Continuous Event Times from Competing Risks
Joint Alaysis of Longitudinal Data and Competing Risks
A Robust Model with t-Distributed Random Errors
Ordinal Longitudinal Outcomes with Missing Data Due to Multiple Failure Types
Bayesian Joint Models with Heterogeneous Random Effects
Accelerated Failure Time Models for Competing Risks
Joint Models for Multivariate Longitudinal and Survival Data
Joint Models for Multivariate Longitudinal Outcomes and an Event Time
Joint Models for Recurrent Events and Longitudinal Data
Joint Models for Multivariate Survival and Longitudinal Data
Further TopicsJoint Models and Missing Data: Assumptions, Sensitivity Analysis, and Diagnostics
Variable Selection in Joint Models
Joint Multistate Models
Joint Models for Cure Rate Survival Data
Sample Size and Power Estimation for Joint Models
Appendices
A Software to Implement Joint Models
Bibliography
Index
Show more
Introduction and ExamplesIntroduction
Methods for Ignorable Missing Data
Introduction
Missing Data Mechanisms
Linear and Generalized Linear Mixed Models
Generalized Estimating Equations
Fruther topics
Time-to-event data analysis
Right censoring
Survival function and hazard function
Estimation of a survival function
Cox's semiparametric multiplicative hazards models
Accelerated failure time models with time-independent covariates
Accelerated failure time model with time-dependent covariates
Methods for competing risks data
Further topics
Overview of Joint Models for Longitudinal and Time-to-Event Data
Joint Models of Longitudinal Data and an Event time
Joint Models with Discrete Event Times and Monotone Missingness
Longitudinal Data with Both Monotone and Intermittent Missing Values
Event Time Models with Intermittently Measured Time Dependent Covariates
Longitudinal Data with Informative Observation Times
Dynamic Prediction in Joint Models
Joint Models for Longitudinal Data and Continuous Event Times from Competing Risks
Joint Alaysis of Longitudinal Data and Competing Risks
A Robust Model with t-Distributed Random Errors
Ordinal Longitudinal Outcomes with Missing Data Due to Multiple Failure Types
Bayesian Joint Models with Heterogeneous Random Effects
Accelerated Failure Time Models for Competing Risks
Joint Models for Multivariate Longitudinal and Survival Data
Joint Models for Multivariate Longitudinal Outcomes and an Event Time
Joint Models for Recurrent Events and Longitudinal Data
Joint Models for Multivariate Survival and Longitudinal Data
Further TopicsJoint Models and Missing Data: Assumptions, Sensitivity Analysis, and Diagnostics
Variable Selection in Joint Models
Joint Multistate Models
Joint Models for Cure Rate Survival Data
Sample Size and Power Estimation for Joint Models
Appendices
A Software to Implement Joint Models
Bibliography
Index
Show moreIntroduction and ExamplesIntroduction
Methods for Ignorable Missing Data
Introduction
Missing Data Mechanisms
Linear and Generalized Linear Mixed Models
Generalized Estimating Equations
Fruther topics
Time-to-event data analysis
Right censoring
Survival function and hazard function
Estimation of a survival function
Cox's semiparametric multiplicative hazards models
Accelerated failure time models with time-independent
covariates
Accelerated failure time model with time-dependent covariates
Methods for competing risks data
Further topics
Overview of Joint Models for Longitudinal and
Time-to-Event Data
Joint Models of Longitudinal Data and an Event time
Joint Models with Discrete Event Times and Monotone Missingness
Longitudinal Data with Both Monotone and Intermittent Missing
Values
Event Time Models with Intermittently Measured Time Dependent
Covariates
Longitudinal Data with Informative Observation Times
Dynamic Prediction in Joint Models
Joint Models for Longitudinal Data and Continuous Event
Times from Competing Risks
Joint Alaysis of Longitudinal Data and Competing Risks
A Robust Model with t-Distributed Random Errors
Ordinal Longitudinal Outcomes with Missing Data Due to Multiple
Failure Types
Bayesian Joint Models with Heterogeneous Random Effects
Accelerated Failure Time Models for Competing Risks
Joint Models for Multivariate Longitudinal and Survival
Data
Joint Models for Multivariate Longitudinal Outcomes and an Event
Time
Joint Models for Recurrent Events and Longitudinal Data
Joint Models for Multivariate Survival and Longitudinal Data
Further TopicsJoint Models and Missing Data:
Assumptions, Sensitivity Analysis, and Diagnostics
Variable Selection in Joint Models
Joint Multistate Models
Joint Models for Cure Rate Survival Data
Sample Size and Power Estimation for Joint Models
Appendices
A Software to Implement Joint Models
Bibliography
Index
Robert Elashoff, Gang Li, Ning Li
"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. … [T]his book is a
comprehensive review of the existing literature on joint models,
including most extensions of these models, fully parametric or not,
for multiple events and multiple markers with a special focus on
missingness problems and details about various estimation methods.
By emphasizing the most advanced methods, this book usefully
completes existing monographs on joint models and will be a helpful
reference book for researchers in biostatistics and experienced
statisticians, while applied statisticians could also be interested
thanks to the numerous examples of real data analyses."
—Helene Jacqmin-Gadda, University of Bordeaux, in Biometrics, March
2018"This book provides an extensive survey of research performed
on the subject of joint models in longitudinal and time-to-event
data. … The authors’ expertise in this area shines through their
careful attention to detail in presenting the wide variety of
settings in which these models can be applied. Overall, I consider
the book to be a valuable and rich resource for introducing and
promoting this relatively new area of research. … Where this book
primarily succeeds is in the great care taken by the authors in
walking through the necessary details of these joint models and the
breadth of topics they cover. When topics are left out, the authors
refer to a large body of literature to which the interested reader
can look to further their understanding. …
I would recommend it either as a handy reference for researchers or
as a graduate level reference text in a specialized course … [I]t
is truly rich with useful content that can be extracted and applied
with due diligence. …. I certainly consider it a valuable addition
to my bookshelf for personal reference and, should the need arise,
I would be happy to refer it to
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