Dataset Information

Swpermute: Permutation tests for Stepped-Wedge Cluster-Randomised Trials.

ABSTRACT: Permutation tests are useful in stepped-wedge trials to provide robust statistical tests of intervention-effect estimates. However, the Stata command permute does not produce valid tests in this setting because individual observations are not exchangeable. We introduce the swpermute command that permutes clusters to sequences to maintain exchangeability. The command provides additional functionality to aid users in performing analyses of stepped-wedge trials. In particular, we include the option "withinperiod" that performs the specified analysis separately in each period of the study with the resulting period-specific intervention-effect estimates combined as a weighted average. We also include functionality to test non-zero null hypotheses to aid the construction of confidence intervals. Examples of the application of swpermute are given using data from a trial testing the impact of a new tuberculosis diagnostic test on bacterial confirmation of a tuberculosis diagnosis.

SUBMITTER: Thompson J

PROVIDER: S-EPMC7305031 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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swpermute: Permutation tests for Stepped-Wedge Cluster-Randomised Trials.

Thompson Jennifer J Davey Calum C Hayes Richard R Hargreaves James J Fielding Katherine K

The Stata journal 20191218 4

Permutation tests are useful in stepped-wedge trials to provide robust statistical tests of intervention-effect estimates. However, the Stata command permute does not produce valid tests in this setting because individual observations are not exchangeable. We introduce the swpermute command that permutes clusters to sequences to maintain exchangeability. The command provides additional functionality to aid users in performing analyses of stepped-wedge trials. In particular, we include the option ...[more]

PMID: 32565746

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Project description:BACKGROUND:Cluster randomised trials with unequal sized clusters often have lower precision than with clusters of equal size. To allow for this, sample sizes are inflated by a modified version of the design effect for clustering. These inflation factors are valid under the assumption that randomisation is stratified by cluster size. We investigate the impact of unequal cluster size when that constraint is relaxed, with particular focus on the stepped-wedge cluster randomised trial, where this is more difficult to achieve. METHODS:Assuming a multi-level mixed effect model with exchangeable correlation structure for a cross-sectional design, we use simulation methods to compare the precision for a trial with clusters of unequal size to a trial with clusters of equal size (relative efficiency). For a range of scenarios we illustrate the impact of various design features (the cluster-mean correlation - a function of the intracluster correlation and the cluster size, the number of clusters, number of randomisation sequences) on the average and distribution of the relative efficiency. RESULTS:Simulations confirm that the average reduction in precision, due to varying cluster sizes, is smaller in a stepped-wedge trial compared to the parallel trial. However, the variance of the distribution of the relative efficiency is large; and is larger under the stepped-wedge design compared to the parallel design. This can result in large variations in actual power, depending on the allocation of clusters to sequences. Designs with larger variations in cluster sizes, smaller number of clusters and studies with smaller cluster-mean correlations (smaller cluster sizes or smaller intra-cluster correlation) are particularly at risk. CONCLUSION:The actual realised power in a stepped-wedge trial might be substantially higher or lower than that estimated. This is particularly important when there are a small number of clusters or the variability in cluster sizes is large. Constraining the randomisation on cluster size, where feasible, might mitigate this effect.

Project description:ObjectiveTo test whether providing relevant clinical trial registry information to peer reviewers evaluating trial manuscripts decreases discrepancies between registered and published trial outcomes.DesignStepped wedge, cluster-randomised trial, with clusters comprised of eligible manuscripts submitted to each participating journal between 1 November 2018 and 31 October 2019.SettingThirteen medical journals.ParticipantsManuscripts were eligible for inclusion if they were submitted to a participating journal during the study period, presented results from the primary analysis of a clinical trial, and were peer reviewed.InterventionsDuring the control phase, there were no changes to pre-existing peer review practices. After journals crossed over into the intervention phase, peer reviewers received a data sheet describing whether trials were registered, the initial registration and enrolment dates, and the registered primary outcome(s) when enrolment began.Main outcome measureThe presence of a clearly defined, prospectively registered primary outcome consistent with the primary outcome in the published trial manuscript, as determined by two independent outcome assessors.ResultsWe included 419 manuscripts (243 control and 176 intervention). Participating journals published 43% of control-phase manuscripts and 39% of intervention-phase manuscripts (model-estimated percentage difference between intervention and control trials = -10%, 95% CI -25% to 4%). Among the 173 accepted trials, published primary outcomes were consistent with clearly defined, prospectively registered primary outcomes in 40 of 105 (38%) control-phase trials and 27 of 68 (40%) intervention-phase trials. A linear mixed model did not show evidence of a statistically significant primary outcome effect from the intervention (estimated difference between intervention and control=-6% (90% CI -27% to 15%); one-sided p value=0.68).ConclusionsThese results do not support use of the tested intervention as implemented here to increase agreement between prospectively registered and published trial outcomes. Other approaches are needed to improve the quality of outcome reporting of clinical trials.Trial registration numberISRCTN41225307.

Dataset Information

Swpermute: Permutation tests for Stepped-Wedge Cluster-Randomised Trials.

Publications

swpermute: Permutation tests for Stepped-Wedge Cluster-Randomised Trials.

Similar Datasets

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