Journal ArticleDOI
Approximate inference in generalized linear mixed models
Norman E. Breslow,D. G. Clayton +1 more
TLDR
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.Abstract:
Statistical approaches to overdispersion, correlated errors, shrinkage estimation, and smoothing of regression relationships may be encompassed within the framework of the generalized linear mixed model (GLMM). Given an unobserved vector of random effects, observations are assumed to be conditionally independent with means that depend on the linear predictor through a specified link function and conditional variances that are specified by a variance function, known prior weights and a scale factor. The random effects are assumed to be normally distributed with mean zero and dispersion matrix depending on unknown variance components. For problems involving time series, spatial aggregation and smoothing, the dispersion may be specified in terms of a rank deficient inverse covariance matrix. Approximation of the marginal quasi-likelihood using Laplace's method leads eventually to estimating equations based on penalized quasilikelihood or PQL for the mean parameters and pseudo-likelihood for the variances. Im...read more
Citations
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Journal ArticleDOI
On the effect of the number of quadrature points in a logistic random effects model: an example
Emmanuel Lesaffre,Bart Spiessens +1 more
TL;DR: In this paper, a logistic random-intercepts model was used in the context of a longitudinal clinical trial where the Gauss-Hermite method gave valid results only for a high number of quadrature points (Q).
Journal ArticleDOI
Seizure occurrence: Precipitants and prediction
TL;DR: Lack of sleep and higher self-reported stress and anxiety levels were associated with seizure occurrence, and seizure prediction based on precipitants, premonitory features, and self-prediction may provide a foundation for preemptive treatment.
Journal ArticleDOI
A Bayesian analysis of mixed survival models
Vincent Ducrocq,George Casella +1 more
TL;DR: Pour l'estimation des parametres τ de la distribution des termes aleatoires, une analyse bayesienne est proposede, en comparant les resultats de l'approximation de π(τ) avec ceux obtenus apres integration algebrique.
Journal ArticleDOI
Variable DNA Methylation Is Associated with Chronic Obstructive Pulmonary Disease and Lung Function
Weiliang Qiu,Andrea A. Baccarelli,Vincent J. Carey,Nadia Boutaoui,Helene Bacherman,Barbara J. Klanderman,Stephen I. Rennard,Alvar Agusti,Wayne H. Anderson,David A. Lomas,Dawn L. DeMeo +10 more
TL;DR: A large-scale analysis of methylation marks in DNA from subjects well phenotyped for nonneoplastic lung disease suggests that DNA methylation may be a biomarker of COPd and may highlight new pathways of COPD pathogenesis.
Journal ArticleDOI
Improved estimation procedures for multilevel models with binary response: a case-study
German Rodriguez,Noreen Goldman +1 more
TL;DR: In this paper, the authors fit three-level random-intercept models to actual data for two binary outcomes, to assess whether refined approximation procedures, namely penalized quasi-likelihood and second-order improvements to marginal and penalized likelihood, also underestimate the underlying parameters.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
Generalized Linear Models
Peter McCullagh,John A. Nelder +1 more
TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
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.