Corrigendum to “Impact of baseline covariate imbalance on bias in treatment effect estimation in cluster randomized trials: Race as an example” [Contemporary Clinical Trials volume 88 (2020) 105775]

2021 ◽  
Vol 103 ◽  
pp. 106298
Author(s):  
Siyun Yang ◽  
Monique Anderson Starks ◽  
Adrian F. Hernandez ◽  
Elizabeth L. Turner ◽  
Robert M. Califf ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Treatment Effect ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Treatment Effect Estimation ◽  
Effect Estimation ◽  
Cluster Randomized

scholarly journals Impact of baseline covariate imbalance on bias in treatment effect estimation in cluster randomized trials: Race as an example

2020 ◽  
Vol 88 ◽  
pp. 105775 ◽  
Author(s):  
Siyun Yang ◽  
Monique Anderson Starks ◽  
Adrian F. Hernandez ◽  
Elizabeth L. Turner ◽  
Robert M. Califf ◽  
...  
Keyword(s):  
Treatment Effect ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Treatment Effect Estimation ◽  
Effect Estimation ◽  
Cluster Randomized

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.

scholarly journals Assessing heterogeneity of treatment effect analyses in health-related cluster randomized trials: A systematic review

PLoS ONE ◽  
2019 ◽  
Vol 14 (8) ◽  
pp. e0219894
Author(s):  
Monique Anderson Starks ◽  
Gillian D. Sanders ◽  
Remy Rene Coeytaux ◽  
Isaretta L. Riley ◽  
Larry R. Jackson ◽  
...  
Keyword(s):  
Systematic Review ◽  
Treatment Effect ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Heterogeneity Of Treatment Effect ◽  
Health Related ◽  
Cluster Randomized
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