scholarly journals Sample size requirements for detecting treatment effect heterogeneity in cluster randomized trials

2020 ◽  
Vol 39 (28) ◽  
pp. 4218-4237
Author(s):  
Siyun Yang ◽  
Fan Li ◽  
Monique A. Starks ◽  
Adrian F. Hernandez ◽  
Robert J. Mentz ◽  
...  
Keyword(s):  
Sample Size ◽  
Treatment Effect ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Treatment Effect Heterogeneity ◽  
Cluster Randomized ◽  
Effect Heterogeneity

Inference for the treatment effect in longitudinal cluster randomized trials when treatment effect heterogeneity is ignored

2021 ◽  
pp. 096228022110417
Author(s):  
Rhys Bowden ◽  
Andrew B Forbes ◽  
Jessica Kasza
Keyword(s):  
Treatment Effect ◽  
Randomized Trials ◽  
Error Rates ◽  
Type I ◽  
Cluster Randomized Trials ◽  
Stepped Wedge ◽  
Treatment Effect Heterogeneity ◽  
Cluster Randomized ◽  
Effect Heterogeneity ◽  
The Impact

In cluster-randomized trials, sometimes the effect of the intervention being studied differs between clusters, commonly referred to as treatment effect heterogeneity. In the analysis of stepped wedge and cluster-randomized crossover trials, it is possible to include terms in outcome regression models to allow for such treatment effect heterogeneity yet this is not frequently considered. Outside of some simulation studies of specific cases where the outcome is binary, the impact of failing to include terms for treatment effect heterogeneity on the variance of the treatment effect estimator is unknown. We analytically examine the impact of failing to include terms for treatment effect heterogeneity on the variance of the treatment effect estimator, when outcomes are continuous. Using analysis of variance and feasible generalized least squares we provide expressions for this variance. For both the cluster-randomized crossover design and the stepped wedge design, our analytic derivations indicate that failing to include treatment effect heterogeneity results in the estimates for variance of the treatment effect that are too small, leading to inflation of type I error rates. We therefore recommend assessing the sensitivity of sample size calculations and conclusions drawn from the analysis of cluster randomized trials to the inclusion of treatment effect heterogeneity.

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
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