Performance of analytical methods for overdispersed counts in cluster randomized trials: Sample size, degree of clustering and imbalance

2009 ◽  
Vol 28 (24) ◽  
pp. 2989-3011 ◽  
Author(s):  
Gonzalo Durán Pacheco ◽  
Jan Hattendorf ◽  
John M. Colford ◽  
Daniel Mäusezahl ◽  
Thomas Smith
Keyword(s):  
Sample Size ◽  
Analytical Methods ◽  
Randomized Trials ◽  
Cluster Randomized 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 calculation in cost-effectiveness cluster randomized trials: optimal and maximin approaches

2014 ◽  
Vol 33 (15) ◽  
pp. 2538-2553 ◽  
Author(s):  
Md. Abu Manju ◽  
Math J. J. M. Candel ◽  
Martijn P. F. Berger
Keyword(s):  
Cost Effectiveness ◽  
Sample Size ◽  
Randomized Trials ◽  
Sample Size Calculation ◽  
Cluster Randomized Trials ◽  
Cluster Randomized

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

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

scholarly journals A new dependence parameter approach to improve the design of cluster randomized trials with binary outcomes

2011 ◽  
Vol 8 (6) ◽  
pp. 687-698 ◽  
Author(s):  
Catherine M Crespi ◽  
Weng Kee Wong ◽  
Sheng Wu
Keyword(s):  
Sample Size ◽  
Randomized Trials ◽  
Pearson Correlation ◽  
Sample Size Determination ◽  
Binary Outcomes ◽  
Alternative Measure ◽  
Cluster Randomized Trials ◽  
Sample Size Calculations ◽  
Cluster Randomized ◽  
Measure Of Dependence

Background and Purpose Power and sample size calculations for cluster randomized trials require prediction of the degree of correlation that will be realized among outcomes of participants in the same cluster. This correlation is typically quantified as the intraclass correlation coefficient (ICC), defined as the Pearson correlation between two members of the same cluster or proportion of the total variance attributable to variance between clusters. It is widely known but perhaps not fully appreciated that for binary outcomes, the ICC is a function of outcome prevalence. Hence, the ICC and the outcome prevalence are intrinsically related, making the ICC poorly generalizable across study conditions and between studies with different outcome prevalences. Methods We use a simple parametrization of the ICC that aims to isolate that part of the ICC that measures dependence among responses within a cluster from the outcome prevalence. We incorporate this parametrization into sample size calculations for cluster randomized trials and compare our method to the traditional approach using the ICC. Results Our dependence parameter, R, may be less influenced by outcome prevalence and has an intuitive meaning that facilitates interpretation. Estimates of R from previous studies can be obtained using simple statistics. Comparison of methods showed that the traditional ICC approach to sample size determination tends to overpower studies under many scenarios, calling for more clusters than truly required. Limitations The methods are developed for equal-sized clusters, whereas cluster size may vary in practice. Conclusions The dependence parameter R is an alternative measure of dependence among binary outcomes in cluster randomized trials that has a number of advantages over the ICC.

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