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Diagnostics for generalised linear mixed models [PDF]

Generalized linear mixed models are generalized linear models that include random effects varying between clusters or 'higher-level' units of hierarchically structured data. Such models can be estimated using gllamm.
Sophia Rabe-Hesketh, Anders Skrondal
core  

GENERALIZED LINEAR MIXED MODELS - AN OVERVIEW [PDF]

, 1994
Generalized linear models provide a methodology for doing regression and ANOV A-type analysis with data whose errors are not necessarily normally-distributed.
S. D. Kachman, Stroup, W. W., W. W. Stroup, Kachman, S. D.   +3 more
core   +1 more source

Generalized linear mixed-models.

, 2022
Generalized linear mixed-models.
Adele Diederich (131786), Marc Wyszynski (9046218)   +1 more
core   +1 more source

Bayesian Variable Selection for Pareto Regression Models with Latent Multivariate Log Gamma Process with Applications to Earthquake Magnitudes

Geosciences, 2019
Generalized linear models are routinely used in many environment statistics problems such as earthquake magnitudes prediction. Hu et al. proposed Pareto regression with spatial random effects for earthquake magnitudes.
Hou-Cheng Yang, Guanyu Hu, Ming-Hui Chen
doaj   +1 more source

Elevated Connectivity During Language Processing Is Associated With Cognitive Performance in SeLECTS

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Self‐Limited Epilepsy with Centrotemporal Spikes (SeLECTS) is associated with language impairments despite seizures originating in the motor cortex, suggesting aberrant cross‐network interactions. Here we tested whether functional connectivity in SeLECTS during language tasks predicts language performance.
Wendy Qi, Katharine Lee, Kerry C. Nix, Miguel Menchaca, Xiwei Shé, Lorelei Santa Maria, Wei Wu, Zihuai He, Fiona M. Baumer   +8 more
wiley   +1 more source

Enhancing Cardiovascular Risk Prediction: Development of an Advanced Xgboost Model with Hospital-Level Random Effects

Bioengineering
Background: Ensemble tree-based models such as Xgboost are highly prognostic in cardiovascular medicine, as measured by the Clinical Effectiveness Metric (CEM). However, their ability to handle correlated data, such as hospital-level effects, is limited.
Tim Dong, Iyabosola Busola Oronti, Shubhra Sinha, Alberto Freitas, Bing Zhai, Jeremy Chan, Daniel P. Fudulu, Massimo Caputo, Gianni D. Angelini   +8 more
doaj   +1 more source

Ofatumumab in Myelin Oligodendrocyte Glycoprotein Antibody–Associated Disease: A Comparison With Rituximab

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To evaluate the efficacy and safety of ofatumumab in patients with myelin oligodendrocyte glycoprotein antibody–associated disease (MOGAD), and compare it with rituximab. Methods We conducted a single–center, observational study including 22 MOGAD patients treated with ofatumumab and 21 treated with rituximab.
Yuxin Fan, Lei Zhou, Jingzi ZhangBao, Hongmei Tan, Zhouzhou Wang, Chao Quan   +5 more
wiley   +1 more source

MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package [PDF]

Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in closed form.
Jarrod D. Hadfield
core  

Memory and Resting‐State Connectivity in Acute Transient Global Amnesia: A Case–Control fMRI Study

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Objectives Transient global amnesia (TGA) is a striking model of isolated amnesia. While hippocampal lesions are well described, the network‐level mechanisms and the precise neuropsychological profile remain debated. Our objective was thus to characterize functional and neuropsychological correlates of acute TGA and their ...
Elias El Otmani, Emmanuel Barbeau, Béatrice Lemesle, Emilie Milongo‐Rigal, Sophie Fernandez, Sandrine Charpentier, Jean‐François Albucher, Nicolas Raposo, Fabrice Bonneville, Patrice Péran, Jérémie Pariente   +10 more
wiley   +1 more source

Hierarchical Generalized Linear Models: The R Package HGLMMM [PDF]

The R package HGLMMM has been developed to fit generalized linear models with random effects using the h-likelihood approach. The response variable is allowed to follow a binomial, Poisson, Gaussian or gamma distribution.
Marek Molas, Emmanuel Lesaffre
core