The stepped wedge cluster randomized trial always requires fewer clusters but not always fewer measurements, that is, participants than a parallel cluster randomized trial in a cross-sectional design

Hoop, Esther de; Woertman, Willem; Teerenstra, Steven

doi:10.1016/j.jclinepi.2013.07.008

Cited by 23 publications

(25 citation statements)

References 2 publications

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“…It is widely regarded that the SW-CRT is more powerful than a traditional cluster RCT [3, 7, 10, 11]. Although this has now been proven to be not universally the case [12], we suspect that this belief has contributed to the large number of stepped wedge studies with very few clusters.…”

Section: Discussionmentioning

confidence: 92%

Minimum number of clusters and comparison of analysis methods for cross sectional stepped wedge cluster randomised trials with binary outcomes: A simulation study

Barker

DʼEste

Campbell

et al. 2017

Trials

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BackgroundStepped wedge cluster randomised trials frequently involve a relatively small number of clusters. The most common frameworks used to analyse data from these types of trials are generalised estimating equations and generalised linear mixed models. A topic of much research into these methods has been their application to cluster randomised trial data and, in particular, the number of clusters required to make reasonable inferences about the intervention effect. However, for stepped wedge trials, which have been claimed by many researchers to have a statistical power advantage over the parallel cluster randomised trial, the minimum number of clusters required has not been investigated.MethodsWe conducted a simulation study where we considered the most commonly used methods suggested in the literature to analyse cross-sectional stepped wedge cluster randomised trial data. We compared the per cent bias, the type I error rate and power of these methods in a stepped wedge trial setting with a binary outcome, where there are few clusters available and when the appropriate adjustment for a time trend is made, which by design may be confounding the intervention effect.ResultsWe found that the generalised linear mixed modelling approach is the most consistent when few clusters are available. We also found that none of the common analysis methods for stepped wedge trials were both unbiased and maintained a 5% type I error rate when there were only three clusters.ConclusionsOf the commonly used analysis approaches, we recommend the generalised linear mixed model for small stepped wedge trials with binary outcomes. We also suggest that in a stepped wedge design with three steps, at least two clusters be randomised at each step, to ensure that the intervention effect estimator maintains the nominal 5% significance level and is also reasonably unbiased.Electronic supplementary materialThe online version of this article (doi:10.1186/s13063-017-1862-2) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 92%

Minimum number of clusters and comparison of analysis methods for cross sectional stepped wedge cluster randomised trials with binary outcomes: A simulation study

Barker

DʼEste

Campbell

et al. 2017

Trials

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“…This approach can inform the choice between any two candidate designs: for example, that between a stepped‐wedge and parallel design 14, 16, 17, 18, 19, 20, 21, 22. From (4) the stepped‐wedge is the more efficient only if R > r 0 where the threshold r 0 satisfies

a_{S W} - b_{S W} r_{0} = a_{P D} - b_{P D} r_{0} .

…”

Section: The Precision Of the Effect Estimate Under A Linear Mixed Efmentioning

confidence: 99%

Statistical efficiency and optimal design for stepped cluster studies under linear mixed effects models

Girling

Hemming

2016

Statistics in Medicine

113

266

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In stepped cluster designs the intervention is introduced into some (or all) clusters at different times and persists until the end of the study. Instances include traditional parallel cluster designs and the more recent stepped‐wedge designs. We consider the precision offered by such designs under mixed‐effects models with fixed time and random subject and cluster effects (including interactions with time), and explore the optimal choice of uptake times. The results apply both to cross‐sectional studies where new subjects are observed at each time‐point, and longitudinal studies with repeat observations on the same subjects.The efficiency of the design is expressed in terms of a ‘cluster‐mean correlation’ which carries information about the dependency‐structure of the data, and two design coefficients which reflect the pattern of uptake‐times. In cross‐sectional studies the cluster‐mean correlation combines information about the cluster‐size and the intra‐cluster correlation coefficient. A formula is given for the ‘design effect’ in both cross‐sectional and longitudinal studies.An algorithm for optimising the choice of uptake times is described and specific results obtained for the best balanced stepped designs. In large studies we show that the best design is a hybrid mixture of parallel and stepped‐wedge components, with the proportion of stepped wedge clusters equal to the cluster‐mean correlation. The impact of prior uncertainty in the cluster‐mean correlation is considered by simulation. Some specific hybrid designs are proposed for consideration when the cluster‐mean correlation cannot be reliably estimated, using a minimax principle to ensure acceptable performance across the whole range of unknown values. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

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“…There is growing interest in the use of stepped wedge trials to evaluate service delivery and other health interventions delivered at an organisational or institutional level, particularly when a policy decision to implement the intervention across a number of clusters has already been made . In such cases, stepped wedge designs have a practical advantage over parallel group designs when there are only sufficient resources to ‘switch on’ the intervention in a small number of clusters at any given time , and they may also have a statistical power advantage .…”

Section: Introductionmentioning

confidence: 99%

Sample size calculation for stepped wedge and other longitudinal cluster randomised trials

Teerenstra²,

et al. 2016

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The sample size required for a cluster randomised trial is inflated compared with an individually randomised trial because outcomes of participants from the same cluster are correlated. Sample size calculations for longitudinal cluster randomised trials (including stepped wedge trials) need to take account of at least two levels of clustering: the clusters themselves and times within clusters. We derive formulae for sample size for repeated cross-section and closed cohort cluster randomised trials with normally distributed outcome measures, under a multilevel model allowing for variation between clusters and between times within clusters. Our formulae agree with those previously described for special cases such as crossover and analysis of covariance designs, although simulation suggests that the formulae could underestimate required sample size when the number of clusters is small. Whether using a formula or simulation, a sample size calculation requires estimates of nuisance parameters, which in our model include the intracluster correlation, cluster autocorrelation, and individual autocorrelation. A cluster autocorrelation less than 1 reflects a situation where individuals sampled from the same cluster at different times have less correlated outcomes than individuals sampled from the same cluster at the same time. Nuisance parameters could be estimated from time series obtained in similarly clustered settings with the same outcome measure, using analysis of variance to estimate variance components. Copyright © 2016 John Wiley & Sons, Ltd.

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The stepped wedge cluster randomized trial always requires fewer clusters but not always fewer measurements, that is, participants than a parallel cluster randomized trial in a cross-sectional design

Cited by 23 publications

References 2 publications

Minimum number of clusters and comparison of analysis methods for cross sectional stepped wedge cluster randomised trials with binary outcomes: A simulation study

Minimum number of clusters and comparison of analysis methods for cross sectional stepped wedge cluster randomised trials with binary outcomes: A simulation study

Statistical efficiency and optimal design for stepped cluster studies under linear mixed effects models

Sample size calculation for stepped wedge and other longitudinal cluster randomised trials

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