• Publications
  • Influence
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 the
Longitudinal data analysis for discrete and continuous outcomes.
TLDR
A class of generalized estimating equations (GEEs) for the regression parameters is proposed, extensions of those used in quasi-likelihood methods which have solutions which are consistent and asymptotically Gaussian even when the time dependence is misspecified as the authors often expect.
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 of
Models for longitudinal data: a generalized estimating equation approach.
TLDR
This article discusses extensions of generalized linear models for the analysis of longitudinal data in which heterogeneity in regression parameters is explicitly modelled and uses a generalized estimating equation approach to fit both classes of models for discrete and continuous outcomes.
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 of
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 linear
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 variables
Regression analysis for correlated data.
TLDR
Regression analysis is among the most commonly used methods of statistical analysis in public health research, and one example of a regression problem is to identify factors associated with the racial difference in the risk of low birthweight.
Sample size calculations for studies with correlated observations.
TLDR
This paper presents a method to compute sample sizes and statistical powers for studies involving correlated observations, and appeals to a statistic based on the generalized estimating equation method for correlated data.
An overview of methods for the analysis of longitudinal data.
TLDR
Three approaches, marginal, transition and random effects models, are presented with emphasis on the distinct interpretations of their coefficients in the discrete data case, and generalized estimating equations for inferences about marginal models are reviewed.
...
1
2
3
4
5
...