Spatial Confounding in Generalized Estimating Equations

DOI10.1080/00031305.2021.2009372OpenAlexW3217035173MaRDI QIDQ5050832

Howard D. Bondell, Francis K. C. Hui

Publication date: 18 November 2022

Published in: The American Statistician (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00031305.2021.2009372

zbMATH Keywords

working correlationspatial correlationmarginal modelssandwich covariancerestricted spatial regressionunconditional effect

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• PoisNor
• ngspatial

Cites Work

• Longitudinal data analysis using generalized linear models
• Autologistic models with interpretable parameters
• The importance of scale for spatial-confounding bias and precision of spatial regression estimators
• Analysis of binary spatial data by quasi-likelihood estimating equations
• The Bayesian group Lasso for confounded spatial data
• Adding Spatially-Correlated Errors Can Mess Up the Fixed Effect You Love
• A Note on Fitting a Marginal Model to Mixed Effects Log-Linear Regression Data via GEE
• A Generalized Estimating Equations Approach for Spatially Correlated Binary Data: Applications to the Analysis of Neuroimaging Data
• On Deconfounding Spatial Confounding in Linear Models
• Dimension Reduction and Alleviation of Confounding for Spatial Generalized Linear Mixed Models
• PoisNor: An R package for generation of multivariate data with Poisson and normal marginals
• NOTES ON CONTINUOUS STOCHASTIC PHENOMENA
• Restricted Spatial Regression Methods: Implications for Inference
• A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications
• On the Effects of Spatial Confounding in Hierarchical Models
• Restricted spatial regression in practice: geostatistical models, confounding, and robustness under model misspecification

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