Longitudinal Data Analysis
By: Garrett Fitzmaurice (Editor), Marie Davidian (Editor), Geert Verbeke (Editor)
Hardcover | 11 August 2008 | Edition Number 1
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634 Pages
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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 and applications. It also focuses on the assorted challenges that arise in analyzing longitudinal data.
After discussing historical aspects, leading researchers explore four broad themes: parametric modeling, nonparametric and semiparametric methods, joint models, and incomplete data. Each of these sections begins with an introductory chapter that provides useful background material and a broad outline to set the stage for subsequent chapters. Rather than focus on a narrowly defined topic, chapters integrate important research discussions from the statistical literature. They seamlessly blend theory with applications and include examples and case studies from various disciplines.
Destined to become a landmark publication in the field, this carefully edited collection emphasizes statistical models and methods likely to endure in the future. Whether involved in the development of statistical methodology or the analysis of longitudinal data, readers will gain new perspectives on the field.
Industry Reviews
Advances in longitudinal data analysis: An historical perspective | p. 3 |
Parametric modeling of longitudinal data: Introduction and overview | p. 31 |
Generalized estimating equations for longitudinal data analysis | p. 43 |
Generalized linear mixed-effects models | p. 79 |
Non-linear mixed-effects models | p. 107 |
Growth mixture modeling: Analysis with non-Gaussian random effects | p. 143 |
Targets of inference in hierarchical models for longitudinal data | p. 167 |
Non-parametric and semi-parametric regression methods: Introduction and overview | p. 191 |
Non-parametric and semi-parametric regression methods for longitudinal data | p. 199 |
Functional modeling of longitudinal data | p. 223 |
Smoothing spline models for longitudinal data | p. 253 |
Penalized spline models for longitudinal data | p. 291 |
Joint models for longitudinal data: Introduction and overview | p. 319 |
Joint models for continuous and discrete longitudinal data | p. 327 |
Random-effects models for joint analysis of repeated-measurement and time-to-event outcomes | p. 349 |
Joint models for high-dimensional longitudinal data | p. 367 |
Incomplete data: Introduction and overview | p. 395 |
Selection and pattern-mixture models | p. 409 |
Shared-parameter models | p. 433 |
Inverse probability weighted methods | p. 453 |
Multiple imputation | p. 477 |
Sensitivity analysis for incomplete data | p. 501 |
Estimation of the causal effects of time-varying exposures | p. 553 |
Author Index | p. 601 |
Subject Index | p. 613 |
Table of Contents provided by Blackwell. All Rights Reserved. |
ISBN: 9781584886587
ISBN-10: 1584886587
Series: Chapman & Hall/CRC Handbooks of Modern Statistical Methods
Published: 11th August 2008
Format: Hardcover
Language: English
Number of Pages: 634
Audience: College, Tertiary and University
Publisher: Taylor & Francis Ltd
Country of Publication: US
Edition Number: 1
Dimensions (cm): 25.4 x 17.78 x 3.51
Weight (kg): 1.31
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