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Analysis of Longitudinal Data

ISBN-10: 0199676755
ISBN-13: 9780199676750
Edition: 2nd 2013
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Book details

Edition: 2nd
Copyright year: 2013
Publisher: Oxford University Press
Publication date: 3/14/2013
Binding: Paperback
Pages: 400
Size: 6.25" wide x 9.25" long x 1.00" tall
Weight: 0.418
Language: English

Introduction
Longitudinal studies
Examples
Notation
Merits of longitudinal studies
Approaches to longitudinal data analysis
Organization of subsequent chapters
Design considerations
Introduction
Bias
Efficiency
Sample size calculations
Continuous responses
Binary responses
Further reading
Exploring longitudinal data
Introduction
Graphical presentation of longitudinal data
Fitting smooth curves to longitudinal data
Exploring correlation structure
Exploring association amongst categorical responses
Further reading
General linear models for longitudinal data
Motivation
The general linear model, with correlated errors
The uniform correlation model
The exponential correlation model
Two-stage least-squares estimation and random effects models
Weighted least-squares estimation
Maximum likelihood estimation under Gaussian assumptions
Restricted maximum likelihood estimation
Robust estimation of standard errors
Parametric models for covariance structure
Introduction
Models
Pure serial correlation
Serial correlation plus measurement error
Random intercept plus serial correlation plus measurement error
Random effects plus measurement error
Model-fitting
Formulation
Estimation
Inference
Diagnostics
Examples
Estimation of individual trajectories
Further reading
Analysis of variance methods
Preliminaries
Time-by-time ANOVA
Derived variables
Repeated measures
Conclusions
Generalized linear models for longitudinal data
Marginal models
Random effects models
Transition (Markov) models
Contrasting approaches
Inferences
Marginal models
Introduction
Binary responses
The log-linear model
Log-linear models for marginal means
Generalized estimating equations
Examples
Counted responses
Parametric modelling for count data
Generalized estimating equation approach
Sample size calculations revisited
Further reading
Random effects models
Introduction
Estimation for generalized linear mixed models
Conditional likelihood
Maximum likelihood estimation
Logistic regression for binary responses
Conditional likelihood approach
Random effects models for binary data
Examples of logistic models with Gaussian random effects
Counted responses
Conditional likelihood method
Random effects models for counts
Poisson-Gaussian random effects models
Further reading
Transition models
General
Fitting transition models
Transition models for categorical data
Indonesian children's study example
Ordered categorical data
Log-linear transition models for count data
Further reading
Likelihood-based methods for categorical data
Introduction
Notation and definitions
Generalized linear mixed models
Maximum likelihood algorithms
Bayesian methods
Marginalized models
An example using the Gaussian linear model
Marginalized log-linear models
Marginalized latent variable models
Marginalized transition models
Summary
Examples
Crossover data
Madras schizophrenia data
Summary and further reading
Time-dependent covariates
Introduction
An example: the MSCM study
Stochastic covariates
Estimation issues with cross-sectional models
A simulation illustration
MSCM data and cross-sectional analysis
Summary
Lagged covariates
A single lagged covariate
Multiple lagged covariates
MSCM data and lagged covariates
Summary
Time-dependent confounders
Feedback: response is an intermediate and a confounder
MSCM data and endogeneity
Targets of inference
Estimation using g-computation
MSCM data and g-computation
Estimation using inverse probability of treatment, weights (IPTW)
MSCM data and marginal structural models using IPTW
Summary
Summary and further reading
Missing values in longitudinal data
Introduction
Classification of missing value mechanisms
Intermittent missing values and dropouts
Simple solutions and their limitations
Last observation carried forward
Complete case analysis
Testing for completely random dropouts
Generalized estimating equations under a random missingness mechanism
Modelling the dropout process
Selection models
Pattern mixture models
Random effect models
Contrasting assumptions: a graphical representation
A longitudinal trial of drug therapies for schizophrenia
Discussion
Additional topics
Non-parametric modelling of the mean response
Further reading
Non-linear regression modelling
Correlated errors
Non-linear random effects
Joint modelling of longitudinal measurements and recurrent events
Multivariate longitudinal data
Appendix Statistical background
Introduction
The linear model and the method of least squares
Multivariate Gaussian theory
Likelihood inference
Generalized linear models
Logistic regression
Poisson regression
The general class
Quasi-likelihood
Bibliography
Index

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