Evidence map›Paper›PMID 30402914›Full record

ArticleStatistics in medicine2019

Admissible multiarm stepped-wedge cluster randomized trial designs.

Michael J Grayling, Adrian P Mander, James M S Wason

Abstract read
In one paragraph

Article in Statistics in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
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  3. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Michael J GraylingHub for Trials Methodology Research, MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.ORCID 0000-0002-0680-6668
Adrian P ManderHub for Trials Methodology Research, MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.ORCID 0000-0002-0742-9040
James M S WasonHub for Trials Methodology Research, MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.

Funding

Medical Research Council MC_UP_1302/2Medical Research Council MC_UU_00002/6National Institute for Health Research Cambridge Biomedical Research Centre MC_UP_1302/6
6 · The paper itself

Abstract

Numerous publications have now addressed the principles of designing, analyzing, and reporting the results of stepped-wedge cluster randomized trials. In contrast, there is little research available pertaining to the design and analysis of multiarm stepped-wedge cluster randomized trials, utilized to evaluate the effectiveness of multiple experimental interventions. In this paper, we address this by explaining how the required sample size in these multiarm trials can be ascertained when data are to be analyzed using a linear mixed model. We then go on to describe how the design of such trials can be optimized to balance between minimizing the cost of the trial and minimizing some function of the covariance matrix of the treatment effect estimates. Using a recently commenced trial that will evaluate the effectiveness of sensor monitoring in an occupational therapy rehabilitation program for older persons after hip fracture as an example, we demonstrate that our designs could reduce the number of observations required for a fixed power level by up to 58%. Consequently, when logistical constraints permit the utilization of any one of a range of possible multiarm stepped-wedge cluster randomized trial designs, researchers should consider employing our approach to optimize their trials efficiency.

Indexed as

Cluster AnalysisLinear ModelsSample SizeComputer SimulationHip FracturesHumansRandomized Controlled Trials as TopicResearch Designadmissible designcluster randomized trialmultiple comparisonsoptimal designstepped-wedge

Identifiers

PMID30402914
PMCPMC6491976

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.