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Longitudinal data analysis using generalized linear models
SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of theExpand
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Longitudinal data analysis for discrete and continuous outcomes.
TLDR
A class of generalized estimating equations (GEEs) for the regression parameters is proposed which are consistent and asymptotically Gaussian even when the time dependence is misspecified as we often expect. Expand
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Asymptotic Properties of Maximum Likelihood Estimators and Likelihood Ratio Tests under Nonstandard Conditions
Abstract Large sample properties of the likelihood function when the true parameter value may be on the boundary of the parameter space are described. Specifically, the asymptotic distribution ofExpand
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Analysis of Longitudinal Data.
Correspondence analysis is an exploratory tool for the analysis of associations between categorical variables, the results of which may be displayed graphically. For longitudinal data, two types ofExpand
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Models for longitudinal data: a generalized estimating equation approach.
This article discusses extensions of generalized linear models for the analysis of longitudinal data. Two approaches are considered: subject-specific (SS) models in which heterogeneity in regressionExpand
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Analysis of Longitudinal Data
1. Introduction 2. Design considerations 3. Exploring longitudinal data 4. General linear models 5. Parametric models for covariance structure 6. Analysis of variance methods 7. Generalized linearExpand
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Multivariate Regression Analyses for Categorical Data
SUMMARY It is common to observe a vector of discrete and/or continuous responses in scientific problems where the objective is to characterize the dependence of each response on explanatory variablesExpand
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Regression analysis for correlated data.
Regression analysis is among the most commonly used methods of statistical analysis in public health research. Its objective is to describe the relationship of a response with explanatory variables.Expand
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Sample size calculations for studies with correlated observations.
Correlated data occur frequently in biomedical research. Examples include longitudinal studies, family studies, and ophthalmologic studies. In this paper, we present a method to compute sample sizesExpand
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Modelling multivariate failure time associations in the presence of a competing risk
There has been much research on analysing multivariate failure times, but little that has accommodated failures that arise in the presence of a competing failure process. This paper studies theExpand
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