- 241 pages
- English
- PDF
- Available on iOS & Android
Joint Modeling of Longitudinal and Time-to-Event Data
About this book
Longitudinal studies often incur several problems that challenge standard statistical methods for data analysis. These problems include non-ignorable missing data in longitudinal measurements of one or more response variables, informative observation times of longitudinal data, and survival analysis with intermittently measured time-dependent covariates that are subject to measurement error and/or substantial biological variation. Joint modeling of longitudinal and time-to-event data has emerged as a novel approach to handle these issues.
Joint Modeling of Longitudinal and Time-to-Event Data provides a systematic introduction and review of state-of-the-art statistical methodology in this active research field. The methods are illustrated by real data examples from a wide range of clinical research topics. A collection of data sets and software for practical implementation of the joint modeling methodologies are available through the book website.
This book serves as a reference book for scientific investigators who need to analyze longitudinal and/or survival data, as well as researchers developing methodology in this field. It may also be used as a textbook for a graduate level course in biostatistics or statistics.
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Information
Table of contents
- Cover
- Half Title
- Title Page
- Copyright Page
- Dedication
- Table of Contents
- Preface
- 1: Introduction and Examples
- 2: Methods for Longitudinal Measurements with Ignorable Missing Data
- 3: Methods for Time-to-Event Data
- 4: Overview of Joint Models for Longitudinal and Time-to-Event Data
- 5: Joint Models for Longitudinal Data and Continuous Event Times from Competing Risks
- 6: Joint Models for Multivariate Longitudinal and Survival Data
- 7: Further Topics
- Appendices
- Bibliography
- Index