scholarly journals Estimation and Standardization of Variance Parameters for Planning Cluster-Randomized Trials: A Short Guide for Researchers

2019 ◽  
pp. 178 ◽  
Author(s):  
Metin Bulus ◽  
Sakine Gocer Sahin
Keyword(s):  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Variance Parameters ◽  
Cluster Randomized

scholarly journals Covariate-constrained Randomization Routine for Achieving Baseline Balance in Cluster-randomized Trials

2017 ◽  
Vol 17 (2) ◽  
pp. 503-510 ◽  
Author(s):  
Eva Lorenz ◽  
Sabine Gabrysch
Keyword(s):  
Randomized Trials ◽  
Theoretical Background ◽  
Cluster Randomized Trials ◽  
Cluster Randomized ◽  
Baseline Covariates

In cluster-randomized trials, groups or clusters of individuals, rather than individuals themselves, are randomly allocated to intervention or control. In this article, we describe a new command, ccrand, that implements a covariate-constrained randomization procedure for cluster-randomized trials. It can ensure balance of one or more baseline covariates between trial arms by restriction to allocations that meet specified balance criteria. We provide a brief overview of the theoretical background, describe ccrand and its options, and illustrate it using an example.

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

scholarly journals The problem of imbalance in cluster randomized trials and the benefits of covariate constrained randomization

2021 ◽  
Author(s):  
L Miriam Dickinson ◽  
Patrick Hosokawa ◽  
Jeanette A Waxmonsky ◽  
Bethany M Kwan
Keyword(s):  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Cluster Randomized

xtgeebcv: A command for bias-corrected sandwich variance estimation for GEE analyses of cluster randomized trials

2020 ◽  
Vol 20 (2) ◽  
pp. 363-381 ◽  
Author(s):  
John A. Gallis ◽  
Fan Li ◽  
Elizabeth L. Turner
Keyword(s):  
Randomized Trials ◽  
Variance Estimation ◽  
Public Health Education ◽  
Standard Errors ◽  
Future Research ◽  
Type I ◽  
Cluster Randomized Trials ◽  
Finite Sample ◽  
Individual Level ◽  
Cluster Randomized

Cluster randomized trials, where clusters (for example, schools or clinics) are randomized to comparison arms but measurements are taken on individuals, are commonly used to evaluate interventions in public health, education, and the social sciences. Analysis is often conducted on individual-level outcomes, and such analysis methods must consider that outcomes for members of the same cluster tend to be more similar than outcomes for members of other clusters. A popular individual-level analysis technique is generalized estimating equations (GEE). However, it is common to randomize a small number of clusters (for example, 30 or fewer), and in this case, the GEE standard errors obtained from the sandwich variance estimator will be biased, leading to inflated type I errors. Some bias-corrected standard errors have been proposed and studied to account for this finite-sample bias, but none has yet been implemented in Stata. In this article, we describe several popular bias corrections to the robust sandwich variance. We then introduce our newly created command, xtgeebcv, which will allow Stata users to easily apply finite-sample corrections to standard errors obtained from GEE models. We then provide examples to demonstrate the use of xtgeebcv. Finally, we discuss suggestions about which finite-sample corrections to use in which situations and consider areas of future research that may improve xtgeebcv.

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.

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