Parental views on the principles for cluster randomised trials involving neonates and infants

2018 ◽  
Vol 108 (5) ◽  
pp. 789-791
Author(s):  
Nicole Thiele ◽  
Johanna M. Walz ◽  
Verena Lindacher ◽  
Silke Mader ◽  
Gorm Greisen ◽  
...  
Keyword(s):  
Randomised Trials ◽  
Cluster Randomised Trials ◽  
Cluster Randomised ◽  
Parental Views ◽  
Neonates And Infants

scholarly journals Intra-cluster correlations from the CLustered OUtcome Dataset bank to inform the design of longitudinal cluster trials

2021 ◽  
pp. 174077452110208
Author(s):  
Elizabeth Korevaar ◽  
Jessica Kasza ◽  
Monica Taljaard ◽  
Karla Hemming ◽  
Terry Haines ◽  
...  
Keyword(s):  
Sample Size ◽  
Discrete Time ◽  
Correlation Coefficients ◽  
Time Decay ◽  
Randomised Trials ◽  
Cluster Randomised Trials ◽  
Cluster Randomised ◽  
Sample Size Calculations ◽  
Correlation Structures ◽  
Cluster Correlation

Background: Sample size calculations for longitudinal cluster randomised trials, such as crossover and stepped-wedge trials, require estimates of the assumed correlation structure. This includes both within-period intra-cluster correlations, which importantly differ from conventional intra-cluster correlations by their dependence on period, and also cluster autocorrelation coefficients to model correlation decay. There are limited resources to inform these estimates. In this article, we provide a repository of correlation estimates from a bank of real-world clustered datasets. These are provided under several assumed correlation structures, namely exchangeable, block-exchangeable and discrete-time decay correlation structures. Methods: Longitudinal studies with clustered outcomes were collected to form the CLustered OUtcome Dataset bank. Forty-four available continuous outcomes from 29 datasets were obtained and analysed using each correlation structure. Patterns of within-period intra-cluster correlation coefficient and cluster autocorrelation coefficients were explored by study characteristics. Results: The median within-period intra-cluster correlation coefficient for the discrete-time decay model was 0.05 (interquartile range: 0.02–0.09) with a median cluster autocorrelation of 0.73 (interquartile range: 0.19–0.91). The within-period intra-cluster correlation coefficients were similar for the exchangeable, block-exchangeable and discrete-time decay correlation structures. Within-period intra-cluster correlation coefficients and cluster autocorrelations were found to vary with the number of participants per cluster-period, the period-length, type of cluster (primary care, secondary care, community or school) and country income status (high-income country or low- and middle-income country). The within-period intra-cluster correlation coefficients tended to decrease with increasing period-length and slightly decrease with increasing cluster-period sizes, while the cluster autocorrelations tended to move closer to 1 with increasing cluster-period size. Using the CLustered OUtcome Dataset bank, an RShiny app has been developed for determining plausible values of correlation coefficients for use in sample size calculations. Discussion: This study provides a repository of intra-cluster correlations and cluster autocorrelations for longitudinal cluster trials. This can help inform sample size calculations for future longitudinal cluster randomised trials.

scholarly journals Cluster randomised trials

BMJ ◽  
1999 ◽  
Vol 318 (7193) ◽  
pp. 1286-1286
Author(s):  
N. Freemantle ◽  
J. Wood ◽  
M. K Campbell ◽  
J. M Grimshaw
Keyword(s):  
Randomised Trials ◽  
Cluster Randomised Trials ◽  
Cluster Randomised

scholarly journals Spatial analysis of cluster randomised trials: a systematic review of analysis methods

2017 ◽  
Vol 14 (1) ◽  
Author(s):  
Christopher Jarvis ◽  
Gian Luca Di Tanna ◽  
Daniel Lewis ◽  
Neal Alexander ◽  
W. John Edmunds
Keyword(s):  
Systematic Review ◽  
Spatial Analysis ◽  
Randomised Trials ◽  
Cluster Randomised Trials ◽  
Cluster Randomised ◽  
Analysis Methods

scholarly journals Group sequential designs for stepped-wedge cluster randomised trials

2017 ◽  
Vol 14 (5) ◽  
pp. 507-517 ◽  
Author(s):  
Michael J Grayling ◽  
James MS Wason ◽  
Adrian P Mander
Keyword(s):  
Randomised Trial ◽  
Cluster Randomised Trial ◽  
Type I ◽  
Randomised Trials ◽  
Early Stopping ◽  
Stepped Wedge ◽  
Group Sequential ◽  
Interim Analyses ◽  
Cluster Randomised Trials ◽  
Cluster Randomised

Background/Aims: The stepped-wedge cluster randomised trial design has received substantial attention in recent years. Although various extensions to the original design have been proposed, no guidance is available on the design of stepped-wedge cluster randomised trials with interim analyses. In an individually randomised trial setting, group sequential methods can provide notable efficiency gains and ethical benefits. We address this by discussing how established group sequential methodology can be adapted for stepped-wedge designs. Methods: Utilising the error spending approach to group sequential trial design, we detail the assumptions required for the determination of stepped-wedge cluster randomised trials with interim analyses. We consider early stopping for efficacy, futility, or efficacy and futility. We describe first how this can be done for any specified linear mixed model for data analysis. We then focus on one particular commonly utilised model and, using a recently completed stepped-wedge cluster randomised trial, compare the performance of several designs with interim analyses to the classical stepped-wedge design. Finally, the performance of a quantile substitution procedure for dealing with the case of unknown variance is explored. Results: We demonstrate that the incorporation of early stopping in stepped-wedge cluster randomised trial designs could reduce the expected sample size under the null and alternative hypotheses by up to 31% and 22%, respectively, with no cost to the trial’s type-I and type-II error rates. The use of restricted error maximum likelihood estimation was found to be more important than quantile substitution for controlling the type-I error rate. Conclusion: The addition of interim analyses into stepped-wedge cluster randomised trials could help guard against time-consuming trials conducted on poor performing treatments and also help expedite the implementation of efficacious treatments. In future, trialists should consider incorporating early stopping of some kind into stepped-wedge cluster randomised trials according to the needs of the particular trial.

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