scholarly journals Randomization inference for stepped-wedge cluster-randomized trials: An application to community-based health insurance

2017 ◽  
Vol 11 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Xinyao Ji ◽  
Gunther Fink ◽  
Paul Jacob Robyn ◽  
Dylan S. Small
Keyword(s):  
Health Insurance ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Stepped Wedge ◽  
Community Based ◽  
Community Based Health Insurance ◽  
Randomization Inference ◽  
Cluster Randomized

scholarly journals Impact of unequal cluster sizes for GEE analyses of stepped wedge cluster randomized trials with binary outcomes

2021 ◽  
Author(s):  
Zibo Tian ◽  
John S. Preisser ◽  
Denise Esserman ◽  
Elizabeth L. Turner ◽  
Paul J. Rathouz ◽  
...  
Keyword(s):  
Randomized Trials ◽  
Binary Outcomes ◽  
Cluster Randomized Trials ◽  
Stepped Wedge ◽  
Cluster Randomized ◽  
Unequal Cluster

Randomization inference for balanced cluster-randomized trials

2005 ◽  
Vol 2 (2) ◽  
pp. 130-140 ◽  
Author(s):  
Gillian M Raab ◽  
Isabella Butcherb
Keyword(s):  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Randomization Inference ◽  
Cluster Randomized

scholarly journals Novel methods for the analysis of stepped wedge cluster randomized trials

2019 ◽  
Vol 39 (7) ◽  
pp. 815-844 ◽  
Author(s):  
Lee Kennedy‐Shaffer ◽  
Victor Gruttola ◽  
Marc Lipsitch
Keyword(s):  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Stepped Wedge ◽  
Novel Methods ◽  
Cluster Randomized

scholarly journals Statistical Properties of Stepped Wedge Cluster-Randomized Trials in Infectious Disease Outbreaks

2020 ◽  
Author(s):  
Lee Kennedy-Shaffer ◽  
Marc Lipsitch
Keyword(s):  
Infectious Disease ◽  
Randomized Trials ◽  
Disease Outbreaks ◽  
Statistical Properties ◽  
Cluster Randomized Trials ◽  
Stepped Wedge ◽  
Infectious Disease Outbreaks ◽  
New Methods ◽  
Stepped Wedge Trials ◽  
Cluster Randomized

ABSTRACTRandomized controlled trials are crucial for the evaluation of interventions such as vaccinations, but the design and analysis of these studies during infectious disease outbreaks is complicated by statistical, ethical, and logistical factors. Attempts to resolve these complexities have led to the proposal of a variety of trial designs, including individual randomization and several types of cluster randomization designs: parallel-arm, ring vaccination, and stepped wedge designs. Because of the strong time trends present in infectious disease incidence, however, methods generally used to analyze stepped wedge trials may not perform well in these settings. Using simulated outbreaks, we evaluate various designs and analysis methods, including recently proposed methods for analyzing stepped wedge trials, to determine the statistical properties of these methods. While new methods for analyzing stepped wedge trials can provide some improvement over previous methods, we find that they still lag behind parallel-arm cluster-randomized trials and individually-randomized trials in achieving adequate power to detect intervention effects. We also find that these methods are highly sensitive to the weighting of effect estimates across time periods. Despite the value of new methods, stepped wedge trials still have statistical disadvantages compared to other trial designs in epidemic settings.

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.

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