A combined overdispersed and marginalized multilevel model
Samuel Iddi,Geert Molenberghs +1 more
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TLDR
It turns out that by explicitly allowing for overdispersion random effect, the model significantly improves and is applied to two clinical studies and compared to the existing approach.About:
This article is published in Computational Statistics & Data Analysis.The article was published on 2012-06-01 and is currently open access. It has received 26 citations till now. The article focuses on the topics: Quasi-likelihood & Overdispersion.read more
Citations
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A marginalized zero-inflated Poisson regression model with overall exposure effects.
TL;DR: A marginalized ZIP model approach for independent responses to model the population mean count directly is developed, allowing straightforward inference for overall exposure effects and empirical robust variance estimation for overall log-incidence density ratios.
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Marginalized multilevel hurdle and zero-inflated models for overdispersed and correlated count data with excess zeros.
Wondwosen Kassahun,Thomas Neyens,Geert Molenberghs,Geert Molenberghs,Christel Faes,Geert Verbeke,Geert Verbeke +6 more
TL;DR: Analysis of two datasets showed that accounting for the correlation, overdispersion, and excess zeros simultaneously resulted in a better fit to the data and, more importantly, that omission of any of them leads to incorrect marginal inference and erroneous conclusions about covariate effects.
Journal ArticleDOI
Marginal correlation from an extended random-effects model for repeated and overdispersed counts
TL;DR: It is shown that the proposed extension of the Poisson-normal GLMM strongly outperforms the classical GLMM, and means, variances, and joint probabilities can be expressed in closed form, allowing for exact intra-sequence correlation expressions.
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Relationship between ambient ultraviolet radiation and non-Hodgkin lymphoma subtypes: a U.S. population-based study of racial and ethnic groups
Elizabeth K. Cahoon,Ruth M. Pfeiffer,David C. Wheeler,Juan Arhancet,Shih Wen Lin,Bruce H. Alexander,Martha S. Linet,D. Michal Freedman +7 more
TL;DR: In this article, the authors evaluated the relationship between ambient UVR exposure and subtype-specific non-Hodgkin lymphoma (NHL) incidence for whites, Hispanics and blacks in the United States for years 2001-2010.
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Natural interpretations in Tobit regression models using marginal estimation methods.
Wei Wang,Michael Griswold +1 more
TL;DR: A direct-marginalization approach using a reparameterized link function to model exposure and covariate effects directly on the truncated dependent variable mean is proposed and an alternative average-predicted-value, post-estimation approach which uses model-predictions for each person in a designated reference group under different exposure statuses to estimate covariate-adjusted overall exposure effects is discussed.
References
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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.
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Models for longitudinal data: a generalized estimating equation approach.
TL;DR: 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.
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Models for Discrete Longitudinal Data
Geert Molenberghs,Geert Verbeke +1 more
TL;DR: This paper presents a meta-analysis of generalized Linear Mixed Models for Gaussian Longitudinal Data and its applications to Hierarchical Models and Random-effects Models.
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Generalized linear mixed models a pseudo-likelihood approach
TL;DR: In this article, a pseudo-likelihood estimation procedure is developed to fit this class of mixed models based on an approximate marginal model for the mean response, implemented via iterated fitting of a weighted Gaussian linear mixed model to a modified dependent variable.
Journal ArticleDOI
Categorical Data Analysis, Second Edition
TL;DR: In this paper, Categorical Data Analysis, Second Edition, is presented for categorical data analysis, with a focus on the use of categorical information. pp. 583-584.