Efficiency loss because of varying cluster size in cluster randomized trials is smaller than literature suggests

2012 ◽  
Vol 31 (4) ◽  
pp. 397-400 ◽  
Author(s):  
Gerard J.P. van Breukelen ◽  
Math J.J.M. Candel
Keyword(s):  
Cluster Size ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Efficiency Loss ◽  
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 The Effect of Cluster Size Variability on Statistical Power in Cluster-Randomized Trials

PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0119074 ◽  
Author(s):  
Stephen A. Lauer ◽  
Ken P. Kleinman ◽  
Nicholas G. Reich
Keyword(s):  
Cluster Size ◽  
Statistical Power ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Size Variability ◽  
Cluster Randomized

Sample Size Issues for Cluster Randomized Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2012 ◽  
Vol 8 (4) ◽  
pp. 146-158 ◽  
Author(s):  
Mirjam Moerbeek
Keyword(s):  
Discrete Time ◽  
Cluster Size ◽  
Randomized Trials ◽  
Cluster Randomized Trial ◽  
Smoking Initiation ◽  
Fixed Number ◽  
Cluster Randomized Trials ◽  
Number Of Clusters ◽  
Sufficient Power ◽  
Cluster Randomized

With cluster randomized trials complete groups of subjects are randomized to treatment conditions. An important question might be whether and when the subjects experience a particular event, such as smoking initiation or recovery from disease. In the social sciences the timing of such events is often measured in discrete time by using time intervals. At the planning phase of a cluster randomized trial one should decide on the number of clusters and cluster size such that parameters are estimated accurately and sufficient power on the test on treatment effect is achieved. On basis of a simulation study it is concluded that regression coefficients are estimated more accurately than the variance of the random cluster effect. In addition, it is shown that power increases with cluster size and number of clusters, and that a sufficient power cannot always be achieved by using larger cluster sizes at a fixed number of clusters.

scholarly journals Statistical approaches to computing sample size in cluster randomized trials: a simulation study

2019 ◽  
Author(s):  
Ashutosh Ranjan ◽  
Guangzi Song ◽  
Christopher S Coffey ◽  
Leslie A McClure
Keyword(s):  
Sample Size ◽  
Simulation Study ◽  
Cluster Size ◽  
Randomized Trials ◽  
Sample Size Calculation ◽  
Minimum Variance ◽  
Type I ◽  
Cluster Randomized Trials ◽  
Weighted Analysis ◽  
Cluster Randomized

Abstract Background: Cluster randomized trials, which randomize groups of individuals to an intervention, are common in health services research when one wants to evaluate improvement in a subject's outcome by intervening at an organizational level. For many such trials sample size calculation is performed under the assumption of equal cluster size. Many trials that set out to recruit equal clusters end up with unequal clusters for various reasons. This leads to a misalignment between the method used for sample size calculation and the data analysis, which may affect trial power. Various weighted analysis methods for analyzing cluster means have been suggested to overcome the problem introduced by unbalanced clusters; however, the performance of such methods has not been evaluated extensively.Methods: We examine the use of the general linear model for analysis of clustered randomized trials assuming equal cluster sizes during the planning stage but ending up with unequal clusters. We demonstrate the performance of three approaches using different weights for analyzing the cluster means: (1) the standard analysis of cluster means, (2) weighting by cluster size, and (3) minimum variance weights. Several distributions are used to generate cluster sizes to cover a wide range of patterns of imbalance. The variability in cluster size is measured by the coefficient of variation (CV). By means of a simulation study, we assess the impact of using each of the three analysis methods with respect to type I error and power of the study and how it is affected by the variability in cluster size. Results: Analyses that assumes equal clusters provide a reasonable approximation when cluster sizes vary minimally (CV < 0.30). In an analysis weighted by cluster size type I errors were inflated, and that worsened as the variation in cluster size increases. However, a minimum variance weighted analysis best maintains target power and level of significance under all degrees of imbalance considered. Conclusion: The unweighted analysis works well as an approximate method when the variation in cluster size is minimal. However, using minimum variance weights performs much better across the full range of variation in cluster size and is recommended.

Response to the Letter ‘Efficiency loss due to varying cluster sizes in cluster randomized trials and how to compensate for it: comment on You et al. (2011)’ by van Breukelen and Candel

2012 ◽  
Vol 9 (1) ◽  
pp. 126-127
Author(s):  
Zhiying You ◽  
O Dale Williams ◽  
Inmaculada Aban ◽  
Edmond Kato Kabagambe ◽  
Hemant K Tiwari ◽  
...  
Keyword(s):  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Efficiency Loss ◽  
Cluster Randomized

scholarly journals Statistical approaches to computing sample size in cluster randomized trials: a simulation study

2020 ◽  
Author(s):  
Ashutosh Ranjan ◽  
Guangzi Song ◽  
Christopher S Coffey ◽  
Leslie A McClure
Keyword(s):  
Sample Size ◽  
Cluster Size ◽  
Randomized Trials ◽  
Sample Size Calculation ◽  
Minimum Variance ◽  
Type I ◽  
Cluster Randomized Trials ◽  
Planning Stage ◽  
Cluster Randomized ◽  
The Impact

Abstract Background: Cluster randomized trials, which randomize groups of individuals to an intervention, are common in health services research when one wants to evaluate improvement in a subject's outcome by intervening at an organizational level. For many such trials, sample size calculation is performed under the assumption of equal cluster size. For a variety of reasons, many trials that set out to recruit clusters of the same size end up with unequal clusters. This leads to a misalignment between the method used for sample size calculation and the data analysis, which may affect trial power. Various weighted analysis methods for analyzing cluster means have been suggested to overcome the problem introduced by unbalanced clusters; however, the performance of such methods has not been evaluated extensively. Methods: We examine the use of the general linear model for analysis of clustered randomized trials that assume equal cluster sizes during the planning stage, but for which the realized cluster sizes are unequal. We demonstrate the performance of three approaches using different weights for analyzing the cluster means: (1) the standard analysis of cluster means, (2) weighting by cluster size, and (3) minimum variance weights. Several distributions are used to generate cluster sizes to assess a range of patterns of imbalance. The variability in cluster size is measured by the coefficient of variation (CV). We assess the impact of using each of the three methods of analysis with respect to type I error and power of the study and how each are impacted by the variability in cluster size via simulations. Results: Analyses that assumes equal clusters provide a reasonable approximation when cluster sizes vary minimally (CV < 0.30). For analyses weighted by cluster size type I errors were inflated, and that worsened as the variation in cluster size increases, despite reasonable power. However, minimum variance weighted analyses best maintain target power and level of significance under scenarios considered. Conclusion: Unweighted analyses work well as an approximate method when variation in cluster size is minimal. However, using minimum variance weights performs much better across the full range of variation in cluster size and is recommended.

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