scholarly journals Commentary: Right truncation in cluster randomized trials can attenuate the power of a marginal analysis

2020 ◽  
Vol 49 (3) ◽  
pp. 964-967 ◽  
Author(s):  
Fan Li ◽  
Michael O Harhay
Keyword(s):  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Marginal Analysis ◽  
Cluster Randomized ◽  
Right Truncation

scholarly journals Power calculations for cluster randomized trials (CRTs) with right-truncated Poisson-distributed outcomes: a motivating example from a malaria vector control trial

2020 ◽  
Vol 49 (3) ◽  
pp. 954-962 ◽  
Author(s):  
Lazaro M Mwandigha ◽  
Keith J Fraser ◽  
Amy Racine-Poon ◽  
Mohamad-Samer Mouksassi ◽  
Azra C Ghani
Keyword(s):  
Vector Control ◽  
Malaria Vector ◽  
Randomized Trials ◽  
Full Range ◽  
Adverse Impact ◽  
Design Stage ◽  
Malaria Vector Control ◽  
Cluster Randomized Trials ◽  
Cluster Randomized ◽  
Right Truncation

Abstract Background Cluster randomized trials (CRTs) are increasingly used to study the efficacy of interventions targeted at the population level. Formulae exist to calculate sample sizes for CRTs, but they assume that the domain of the outcomes being considered covers the full range of values of the considered distribution. This assumption is frequently incorrect in epidemiological trials in which counts of infection episodes are right-truncated due to practical constraints on the number of times a person can be tested. Methods Motivated by a malaria vector control trial with right-truncated Poisson-distributed outcomes, we investigated the effect of right-truncation on power using Monte Carlo simulations. Results The results demonstrate that the adverse impact of right-truncation is directly proportional to the magnitude of the event rate, λ, with calculations of power being overestimated in instances where right-truncation was not accounted for. The severity of the adverse impact of right-truncation on power was more pronounced when the number of clusters was ≤30 but decreased the further the right-truncation point was from zero. Conclusions Potential right-truncation should always be accounted for in the calculation of sample size requirements at the study design stage.

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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