Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory

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Yes, you can access Longitudinal Data Analysis by Garrett Fitzmaurice, Marie Davidian, Geert Verbeke, Geert Molenberghs in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher

Chapman and Hall/CRC

Year

2008

ISBN

9781420011579

Edition

Topic

Mathematics

Subtopic

Probability & Statistics

Index

Mathematics

Cover
Title
Copyright
Dedication
Contents
Preface
Editors
Contributors
PART I: Introduction and Historical Overview
CHAPTER 1: Advances in longitudinal data analysis: An historical perspective
PART II: Parametric Modeling of Longitudinal Data
CHAPTER 2: Parametric modeling of longitudinal data: Introduction and overview
CHAPTER 3: Generalized estimating equations for longitudinal data analysis
CHAPTER 4: Generalized linear mixed-effects models
CHAPTER 5: Non-linear mixed-effects models
CHAPTER 6: Growth mixture modeling: Analysis with non-Gaussian random effects
CHAPTER 7: Targets of inference in hierarchical models for longitudinal data
PART III: Non-Parametric and Semi-Parametric Methods for Longitudinal Data
CHAPTER 8: Non-parametric and semi-parametric regression methods: Introduction and overview
CHAPTER 9: Non-parametric and semi-parametric regression methods for longitudinal data
CHAPTER 10: Functional modeling of longitudinal data
CHAPTER 11: Smoothing spline models for longitudinal data
CHAPTER 12: Penalized spline models for longitudinal data
PART IV: Joint Models for Longitudinal Data
CHAPTER 13: Joint models for longitudinal data: Introduction and overview
CHAPTER 14: Joint models for continuous and discrete longitudinal data
CHAPTER 15: Random-effects models for joint analysis of repeated-measurement and time-to-event outcomes
CHAPTER 16: Joint models for high-dimensional longitudinal data
PART V: Incomplete Data
CHAPTER 17: Incomplete data: Introduction and overview
CHAPTER 18: Selection and pattern-mixture models
CHAPTER 19: Shared-parameter models
CHAPTER 20: Inverse probability weighted methods
CHAPTER 21: Multiple imputation
CHAPTER 22: Sensitivity analysis for incomplete data
CHAPTER 23: Estimation of the causal effects of time-varying exposures
Author Index
Subject Index