Conditional Generalized Estimating Equations for the Analysis of Clustered and Longitudinal Data

DOI10.1111/j.1541-0420.2007.00944.xzbMath1170.62068OpenAlexW2092669737WikidataQ33307775 ScholiaQ33307775MaRDI QIDQ3530094

Stijn Vansteelandt, Sylvie Goetgeluk

Publication date: 15 October 2008

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2007.00944.x

zbMATH Keywords

confoundingcorrelated datamarginal models

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Estimation in survival analysis and censored data (62N02)

Related Items

Controlled Direct and Mediated Effects: Definition, Identification and Bounds ⋮ Adjusting for confounding by cluster using generalized linear mixed models ⋮ Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study ⋮ The Mantel-Haenszel estimator adapted for complex survey designs is not dually consistent ⋮ Augmented Designs to Assess Principal Strata Direct Effects ⋮ Estimation of Direct Effects for Survival Data by using the Aalen Additive Hazards Model ⋮ Identification and Efficient Estimation of the Natural Direct Effect among the Untreated ⋮ The role of conditional likelihoods in latent variable modeling

Cites Work

• Unnamed Item
• Longitudinal data analysis using generalized linear models
• Semiparametric Difference-in-Differences Estimators
• Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods
• Effect of Omitted Confounders on the Analysis of Correlated Binary Data
• Between- and Within-Cluster Covariate Effects in the Analysis of Clustered Data
• An Empirical Comparison of Several Clustered Data Approaches Under Confounding Due to Cluster Effects in the Analysis of Complications of Coronary Angioplasty
• Causal Inference with Generalized Structural Mean Models
• Estimating Exposure Effects by Modelling the Expectation of Exposure Conditional on Confounders
• Informative Drop-Out in Longitudinal Data Analysis