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
Influence of experience on performance of individual surgeons in thyroid surgery: prospective cross sectional multicentre study
Antoine Duclos,Jean-Louis Peix,Cyrille Colin,Jean-Louis Kraimps,Fabrice Menegaux,François Pattou,Frederic Sebag,Sandrine Touzet,Stéphanie Bourdy,Nicolas Voirin,Jean-Christophe Lifante +10 more
TL;DR: Optimum individual performance in thyroid surgery cannot be passively achieved or maintained by accumulating experience, and factors contributing to poor performance in very experienced surgeons should be explored further.
Journal ArticleDOI
Joint modeling of survival and longitudinal data : Likelihood approach revisited
TL;DR: Insight is provided into the robustness property of the MLEs against departure from the normal random effects assumption and the difficulty of reliable estimates for the standard errors is suggested by using bootstrap procedures.
Journal ArticleDOI
Genetic association testing using the GENESIS R/Bioconductor package.
Stephanie M. Gogarten,Tamar Sofer,Tamar Sofer,Han Chen,Chaoyu Yu,Jennifer A. Brody,Timothy A. Thornton,Kenneth Rice,Matthew P. Conomos +8 more
TL;DR: GDS format provides efficient storage and retrieval of genotypes measured by microarrays and sequencing, and GENESIS implements highly flexible mixed models, allowing for different link functions, multiple variance components, and phenotypic heteroskedasticity.
Journal ArticleDOI
Markov chain Monte Carlo methods in biostatistics
Andrew Gelman,Donald B. Rubin +1 more
TL;DR: Concerns with implementation should not deter the biostatistician from using MCMC methods, but rather help to ensure wise use of these powerful techniques.
Journal ArticleDOI
The impact of gastrointestinal nematodes on wild reindeer: experimental and cross‐sectional studies
TL;DR: The experimental results show for the first time in a natural ruminant host population that gastrointestinal nematodes can have a significant effect on host condition and fecundity and show that the experimental effects onhost condition and feces is most likely to be due to a negative effect of O. gruehneri.
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.