scholarly journals Ratio estimators of intervention effects on event rates in cluster randomized trials

2021 ◽  
Author(s):  
Xiangmei Ma ◽  
Paul Milligan ◽  
Kwok Fai Lam ◽  
Yin Bun Cheung
Keyword(s):  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Intervention Effects ◽  
Ratio Estimators ◽  
Cluster Randomized ◽  
Event Rates

scholarly journals Leveraging contact network structure in the design of cluster randomized trials

2016 ◽  
Vol 14 (1) ◽  
pp. 37-47 ◽  
Author(s):  
Guy Harling ◽  
Rui Wang ◽  
Jukka-Pekka Onnela ◽  
Victor De Gruttola
Keyword(s):  
Public Health ◽  
Cumulative Incidence ◽  
Randomized Trials ◽  
Health Impact ◽  
Cluster Randomized Trials ◽  
Stepped Wedge ◽  
Epidemic Control ◽  
Intervention Effect ◽  
Intervention Effects ◽  
Cluster Randomized

Background: In settings like the Ebola epidemic, where proof-of-principle trials have provided evidence of efficacy but questions remain about the effectiveness of different possible modes of implementation, it may be useful to conduct trials that not only generate information about intervention effects but also themselves provide public health benefit. Cluster randomized trials are of particular value for infectious disease prevention research by virtue of their ability to capture both direct and indirect effects of intervention, the latter of which depends heavily on the nature of contact networks within and across clusters. By leveraging information about these networks—in particular the degree of connection across randomized units, which can be obtained at study baseline—we propose a novel class of connectivity-informed cluster trial designs that aim both to improve public health impact (speed of epidemic control) and to preserve the ability to detect intervention effects. Methods: We several designs for cluster randomized trials with staggered enrollment, in each of which the order of enrollment is based on the total number of ties (contacts) from individuals within a cluster to individuals in other clusters. Our designs can accommodate connectivity based either on the total number of external connections at baseline or on connections only to areas yet to receive the intervention. We further consider a “holdback” version of the designs in which control clusters are held back from re-randomization for some time interval. We investigate the performance of these designs in terms of epidemic control outcomes (time to end of epidemic and cumulative incidence) and power to detect intervention effect, by simulating vaccination trials during an SEIR-type epidemic outbreak using a network-structured agent-based model. We compare results to those of a traditional Stepped Wedge trial. Results: In our simulation studies, connectivity-informed designs lead to a 20% reduction in cumulative incidence compared to comparable traditional study designs, but have little impact on epidemic length. Power to detect intervention effect is reduced in all connectivity-informed designs, but “holdback” versions provide power that is very close to that of a traditional Stepped Wedge approach. Conclusion: Incorporating information about cluster connectivity in the design of cluster randomized trials can increase their public health impact, especially in acute outbreak settings. Using this information helps control outbreaks—by minimizing the number of cross-cluster infections—with very modest cost in terms of power to detect effectiveness.

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.

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