Incorporating pragmatic features into power analysis for cluster randomized trials with a count outcome

DOI10.1002/sim.8707zbMATH Open1546.62448MaRDI QIDQ6629860

Unnamed Author, Song Zhang, Jing Cao

Publication date: 30 October 2024

Published in: Statistics in Medicine (Search for Journal in Brave)

zbMATH Keywords

pragmaticcount outcomeclustered randomization trialsrandom cluster sizesunequal follow-up

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Longitudinal data analysis using generalized linear models
• A Covariance Estimator for GEE with Improved Small‐Sample Properties
• The cluster bootstrap consistency in generalized estimating equations
• Sample Size Estimation in Cluster Randomized Studies with Varying Cluster Size
• Negative Binomial Regression
• The Analysis of Rates Using Poisson Regression Models
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• A comparison between bootstrap methods and generalized estimating equations for correlated outcomes in generalized linear models
• A Note on the Efficiency of Sandwich Covariance Matrix Estimation
• Relative efficiency of unequal versus equal cluster sizes in cluster randomized trials using generalized estimating equation models
• Some remarks on overdispersion
• Sample size calculation for count outcomes in cluster randomization trials with varying cluster sizes
• The intra-cluster correlation coefficient in cluster randomized trials: a review of definitions
• Sample size considerations for stratified cluster randomization design with binary outcomes and varying cluster size

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