scholarly journals Power and sample size requirements for GEE analyses of cluster randomized crossover trials

2018 ◽  
Vol 38 (4) ◽  
pp. 636-649 ◽  
Author(s):  
Fan Li ◽  
Andrew B. Forbes ◽  
Elizabeth L. Turner ◽  
John S. Preisser
Keyword(s):  
Sample Size ◽  
Crossover Trials ◽  
Cluster Randomized

Relative efficiency and sample size for cluster randomized trials with variable cluster sizes

2010 ◽  
Vol 8 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Zhiying You ◽  
O Dale Williams ◽  
Inmaculada Aban ◽  
Edmond Kato Kabagambe ◽  
Hemant K Tiwari ◽  
...  
Keyword(s):  
Sample Size ◽  
Relative Efficiency ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Variable Cluster ◽  
Cluster Randomized

Sample size estimation for modified Poisson analysis of cluster randomized trials with a binary outcome

2021 ◽  
pp. 096228022199041
Author(s):  
Fan Li ◽  
Guangyu Tong
Keyword(s):  
Relative Risk ◽  
Sample Size ◽  
Poisson Regression ◽  
Cluster Size ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Binomial Regression ◽  
Size Variability ◽  
Working Correlation ◽  
Cluster Randomized

The modified Poisson regression coupled with a robust sandwich variance has become a viable alternative to log-binomial regression for estimating the marginal relative risk in cluster randomized trials. However, a corresponding sample size formula for relative risk regression via the modified Poisson model is currently not available for cluster randomized trials. Through analytical derivations, we show that there is no loss of asymptotic efficiency for estimating the marginal relative risk via the modified Poisson regression relative to the log-binomial regression. This finding holds both under the independence working correlation and under the exchangeable working correlation provided a simple modification is used to obtain the consistent intraclass correlation coefficient estimate. Therefore, the sample size formulas developed for log-binomial regression naturally apply to the modified Poisson regression in cluster randomized trials. We further extend the sample size formulas to accommodate variable cluster sizes. An extensive Monte Carlo simulation study is carried out to validate the proposed formulas. We find that the proposed formulas have satisfactory performance across a range of cluster size variability, as long as suitable finite-sample corrections are applied to the sandwich variance estimator and the number of clusters is at least 10. Our findings also suggest that the sample size estimate under the exchangeable working correlation is more robust to cluster size variability, and recommend the use of an exchangeable working correlation over an independence working correlation for both design and analysis. The proposed sample size formulas are illustrated using the Stop Colorectal Cancer (STOP CRC) trial.

scholarly journals Sample size considerations in the design of cluster randomized trials of combination HIV prevention

2014 ◽  
Vol 11 (3) ◽  
pp. 309-318 ◽  
Author(s):  
Rui Wang ◽  
Ravi Goyal ◽  
Quanhong Lei ◽  
M Essex ◽  
Victor De Gruttola
Keyword(s):  
Hiv Prevention ◽  
Sample Size ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Cluster Randomized

scholarly journals Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

Trials ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Sheila Sprague ◽  
◽  
Taryn Scott ◽  
Shannon Dodds ◽  
David Pogorzelski ◽  
...  
Keyword(s):  
Cluster Identification ◽  
Crossover Trials ◽  
Cluster Randomized

An amendment to this paper has been published and can be accessed via the original article.

A simple sample size formula for analysis of covariance in cluster randomized trials

2012 ◽  
Vol 31 (20) ◽  
pp. 2169-2178 ◽  
Author(s):  
Steven Teerenstra ◽  
Sandra Eldridge ◽  
Maud Graff ◽  
Esther Hoop ◽  
George F. Borm
Keyword(s):  
Sample Size ◽  
Randomized Trials ◽  
Analysis Of Covariance ◽  
Cluster Randomized Trials ◽  
Cluster Randomized
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