Comparison of subject-specific and population averaged models for count data from cluster-unit intervention trials

DOI10.1177/0962280206071931zbMath1122.62322OpenAlexW2048071999WikidataQ31111820 ScholiaQ31111820MaRDI QIDQ5425038

John S. Preisser, Mark Wolfson, Bahjat F. Qaqish, Mary L. Young

Publication date: 7 November 2007

Published in: Statistical Methods in Medical Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1177/0962280206071931

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Related Items (3)

Statistical Implications of Endogeneity Induced by Residential Segregation in Small-Area Modeling of Health Inequities ⋮ Sample size and power considerations for cluster randomized trials with count outcomes subject to right truncation ⋮ A new look at the difference between the GEE and the GLMM when modeling longitudinal count responses

Uses Software

• MLwiN
• Stata
• SAS/STAT

Cites Work

• Unnamed Item
• Longitudinal data analysis using generalized linear models
• Models for Longitudinal Data: A Generalized Estimating Equation Approach
• Estimating Equations for Parameters in Means and Covariances of Multivariate Discrete and Continuous Responses
• Evaluation of Community-Intervention Trials via Generalized Linear Mixed Models
• A Note on the Efficiency of Sandwich Covariance Matrix Estimation
• Marginally Specified Logistic‐Normal Models for Longitudinal Binary Data
• A Generalized Estimating Equations Approach for Spatially Correlated Binary Data: Applications to the Analysis of Neuroimaging Data

This page was built for publication: Comparison of subject-specific and population averaged models for count data from cluster-unit intervention trials