An Empirical Comparison of Several Clustered Data Approaches Under Confounding Due to Cluster Effects in the Analysis of Complications of Coronary Angioplasty

DOI10.1111/J.0006-341X.1999.00470.XzbMath1059.62620OpenAlexW2159939252WikidataQ30650135 ScholiaQ30650135MaRDI QIDQ4666589

Thomas R. Ten Have, Stephen E. Kimmel, Mary D. Sammel, Jesse A. Berlin

Publication date: 13 April 2005

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.0006-341x.1999.00470.x

zbMATH Keywords

clustered dataconfoundingepidemiologic methodscoronary angioplasty

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50)

Related Items (5)

A multivariate variance components model for analysis of covariance in designed experiments ⋮ Adjusting for confounding by cluster using generalized linear mixed models ⋮ Conditional Generalized Estimating Equations for the Analysis of Clustered and Longitudinal Data ⋮ The Evaluation of Treatment When Center-Specific Selection Criteria Vary with Respect to Patient Risk ⋮ On the effect of ignoring correlation in the covariates when fitting linear mixed models

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• Approximate Inference in Generalized Linear Mixed Models
• Bias correction in generalised linear mixed models with a single component of dispersion
• Informative Drop-Out in Longitudinal Data Analysis
• Generalized linear mixed models a pseudo-likelihood approach

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