Journal ArticleDOI
Correlated binary regression with covariates specific to each binary observation.
TLDR
It is argued that binary response models that condition on some or all binary responses in a given "block" are useful for studying certain types of dependencies, but not for the estimation of marginal response probabilities or pairwise correlations.Citations
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Approximate inference in generalized linear mixed models
Norman E. Breslow,D. G. Clayton +1 more
TL;DR: In this paper, generalized linear mixed models (GLMM) are used to estimate the marginal quasi-likelihood for the mean parameters and the conditional variance for the variances, and the dispersion matrix is specified in terms of a rank deficient inverse covariance matrix.
Journal ArticleDOI
Analysis of semiparametric regression models for repeated outcomes in the presence of missing data
TL;DR: In this article, the authors proposed a class of inverse probability of censoring weighted estimators for the parameters of models for the dependence of the mean of a vector of correlated response variables on the vector of explanatory variables in the presence of missing response data.
Book
Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide
TL;DR: The authors discusses the most important techniques available for longitudinal data analysis, from simple techniques such as the paired t-test and summary statistics, to more sophisticated ones such as generalized estimating of equations and mixed model analysis, making a distinction between longitudinal analysis with continuous, dichotomous and categorical outcome variables.
Journal ArticleDOI
Using Generalized Estimating Equations for Longitudinal Data Analysis
TL;DR: The generalized estimating equation (GEE) as mentioned in this paper approach of Zeger and Liang facilitates analysis of data collected in longitudinal, nested, or repeated measures designs, in part because they permit specification of a working correlation matrix that accounts for the form of within-subject correlation of responses on dependent variables of many different distributions, including normal, binomial, and Poisson.
Journal ArticleDOI
Generalized Linear Models with Random Effects; a Gibbs Sampling Approach
Scott L. Zeger,M. Rezaul Karim +1 more
TL;DR: This article cast the generalized linear random effects model in a Bayesian framework and use a Monte Carlo method, the Gibbs sampler, to overcome the current computational limitations, which is flexible to easily accommodate changes in the number of observations.
References
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Journal ArticleDOI
Longitudinal data analysis using generalized linear models
Kung Yee Liang,Scott L. Zeger +1 more
TL;DR: In this article, an extension of generalized linear models to the analysis of longitudinal data is proposed, which gives consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence.
Journal ArticleDOI
Longitudinal data analysis for discrete and continuous outcomes.
Scott L. Zeger,Kung-Yee Liang +1 more
TL;DR: 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.
Journal ArticleDOI
Random-effects models for serial observations with binary response
TL;DR: A general mixed model for the analysis of serial dichotomous responses provided by a panel of study participants, assuming each subject's serial responses are assumed to arise from a logistic model, but with regression coefficients that vary between subjects.
Journal ArticleDOI
An extended quasi-likelihood function
John A. Nelder,D. Pregibon +1 more
TL;DR: In this article, Wedderburn's original definition of quasi-likelihood for generalized linear models is extended to allow the comparison of variance functions as well as those of linear predictors and link functions.
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Longitudinal data analysis for discrete and continuous outcomes.
Scott L. Zeger,Kung-Yee Liang +1 more