Analysis of Longitudinal Data,9780198524847

Analysis of Longitudinal Data

by ; ; ;
Edition: 2nd
ISBN13: 9780198524847
ISBN10: 0198524846
Format: Hardcover
Pub. Date: 2002-08-29
Publisher(s): Oxford University Press
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Summary

The first edition of Analysis for Longitudinal Data has become a classic. Describing the statistical models and methods for the analysis of longitudinal data, it covers both the underlying statistical theory of each method, and its application to a range of examples from the agricultural andbiomedical sciences. The main topics discussed are design issues, exploratory methods of analysis, linear models for continuous data, general linear models for discrete data, and models and methods for handling data and missing values. Under each heading, worked examples are presented in parallelwith the methodological development, and sufficient detail is given to enable the reader to reproduce the author's results using the data-sets as an appendix. This new edition of Analysis for Longitudinal Data provides a thorough and expanded revision of this important text. It includes two newchapters; the first discusses fully parametric models for discrete repeated measures data, and the second explores statistical models for time-dependent predictors.

Table of Contents

Introduction
1(21)
Longitudinal studies
1(2)
Examples
3(12)
Notation
15(1)
Merits of longitudinal studies
16(1)
Approaches to longitudinal data analysis
17(3)
Organization of subsequent chapters
20(2)
Design considerations
22(11)
Introduction
22(1)
Bias
22(2)
Efficiency
24(2)
Sample size calculations
26(5)
Continuous responses
28(2)
Binary responses
30(1)
Further reading
31(2)
Exploring longitudinal data
33(21)
Introduction
33(1)
Graphical presentation of longitudinal data
34(7)
Fitting smooth curves to longitudinal data
41(5)
Exploring correlation structure
46(6)
Exploring association amongst categorical responses
52(1)
Further reading
53(1)
General linear models for longitudinal data
54(27)
Motivation
54(1)
The general linear model with correlated errors
55(4)
The uniform correlation model
55(1)
The exponential correlation model
56(1)
Two-stage least-squares estimation and random effects models
57(2)
Weighted least-squares estimation
59(5)
Maximum likelihood estimation under Gaussian assumptions
64(2)
Restricted maximum likelihood estimation
66(4)
Robust estimation of standard errors
70(11)
Parametric models for covariance structure
81(33)
Introduction
81(1)
Models
82(11)
Pure serial correlation
84(5)
Serial correlation plus measurement error
89(1)
Random intercept plus serial correlation plus measurement error
90(1)
Random effects plus measurement error
91(2)
Model-fitting
93(6)
Formulation
94(1)
Estimation
95(2)
Inference
97(1)
Diagnostics
98(1)
Examples
99(11)
Estimation of individual trajectories
110(3)
Further reading
113(1)
Analysis of variance methods
114(12)
Preliminaries
114(1)
Time-by-time ANOVA
115(1)
Derived variables
116(7)
Repeated measures
123(2)
Conclusions
125(1)
Generalized linear models for longitudinal data
126(15)
Marginal models
126(2)
Random effects models
128(2)
Transition (Markov) models
130(1)
Contrasting approaches
131(6)
Inferences
137(4)
Marginal models
141(28)
Introduction
141(1)
Binary responses
142(6)
The log-linear model
142(1)
Log-linear models for marginal means
143(3)
Generalized estimating equations
146(2)
Examples
148(12)
Counted responses
160(5)
Parametric modelling for count data
160(2)
Generalized estimating equation approach
162(3)
Sample size calculations revisited
165(2)
Further reading
167(2)
Random effects models
169(21)
Introduction
169(2)
Estimation for generalized linear mixed models
171(4)
Conditional likelihood
171(1)
Maximum likelihood estimation
172(3)
Logistic regression for binary responses
175(9)
Conditional likelihood approach
175(3)
Random effects models for binary data
178(2)
Examples of logistic models with Gaussian random effects
180(4)
Counted responses
184(5)
Conditional likelihood method
184(2)
Random effects models for counts
186(2)
Poisson-Gaussian random effects models
188(1)
Further reading
189(1)
Transition models
190(18)
General
190(2)
Fitting transition models
192(2)
Transition models for categorical data
194(10)
Indonesian children's study example
197(4)
Ordered categorical data
201(3)
Log-linear transition models for count data
204(2)
Further reading
206(2)
Likelihood-based methods for categorical data
208(37)
Introduction
208(1)
Notation and definitions
209(1)
Generalized linear mixed models
209(7)
Maximum likelihood algorithms
212(2)
Bayesian methods
214(2)
Marginalized models
216(15)
An example using the Gaussian linear model
218(2)
Marginalized log-linear models
220(2)
Marginalized latent variable models
222(3)
Marginalized transition models
225(6)
Summary
231(1)
Examples
231(12)
Crossover data
231(3)
Madras schizophrenia data
234(9)
Summary and further reading
243(2)
Time-dependent covariates
245(37)
Introduction
245(2)
An example: the MSCM study
247(6)
Stochastic covariates
253(6)
Estimation issues with cross-sectional models
254(2)
A simulation illustration
256(1)
MSCM data and cross-sectional analysis
257(1)
Summary
258(1)
Lagged covariates
259(6)
A single lagged covariate
259(1)
Multiple lagged covariates
260(1)
MSCM data and lagged covariates
261(4)
Summary
265(1)
Time-dependent confounders
265(15)
Feedback: response is an intermediate and a confounder
266(2)
MSCM data and endogeneity
268(1)
Targets of inference
269(4)
Estimation using g-computation
273(2)
MSCM data and g-computation
275(1)
Estimation using inverse probability of treatment weights (IPTW)
276(3)
MSCM data and marginal structural models using IPTW
279(1)
Summary
280(1)
Summary and further reading
280(2)
Missing values in longitudinal data
282(37)
Introduction
282(1)
Classification of missing value mechanisms
283(1)
Intermittent missing values and dropouts
284(3)
Simple solutions and their limitations
287(1)
Last observation carried forward
287(1)
Complete case analysis
288(1)
Testing for completely random dropouts
288(5)
Generalized estimating equations under a random missingness mechanism
293(2)
Modelling the dropout process
295(10)
Selection models
295(4)
Pattern mixture models
299(2)
Random effect models
301(2)
Contrasting assumptions: a graphical representation
303(2)
A longitudinal trial of drug therapies for schizophrenia
305(11)
Discussion
316(3)
Additional topics
319(18)
Non-parametric modelling of the mean response
319(7)
Further reading
326(1)
Non-linear regression modelling
326(3)
Correlated errors
328(1)
Non-linear random effects
329(1)
Joint modelling of longitudinal measurements and recurrent events
329(3)
Multivariate longitudinal data
332(5)
Appendix Statistical background 337(12)
Introduction
337(1)
The linear model and the method of least squares
337(2)
Multivariate Gaussian theory
339(1)
Likelihood inference
340(3)
Generalized linear models
343(3)
Logistic regression
343(1)
Poisson regression
344(1)
The general class
345(1)
Quasi-likelihood
346(3)
Bibliography 349(20)
Index 369

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