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A re-randomisation design for clinical trials

Overview of attention for article published in BMC Medical Research Methodology, November 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

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19 X users

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Title
A re-randomisation design for clinical trials
Published in
BMC Medical Research Methodology, November 2015
DOI 10.1186/s12874-015-0082-2
Pubmed ID
Authors

Brennan C Kahan, Andrew B Forbes, Caroline J Doré, Tim P Morris

Abstract

Recruitment to clinical trials is often problematic, with many trials failing to recruit to their target sample size. As a result, patient care may be based on suboptimal evidence from underpowered trials or non-randomised studies. For many conditions patients will require treatment on several occasions, for example, to treat symptoms of an underlying chronic condition (such as migraines, where treatment is required each time a new episode occurs), or until they achieve treatment success (such as fertility, where patients undergo treatment on multiple occasions until they become pregnant). We describe a re-randomisation design for these scenarios, which allows each patient to be independently randomised on multiple occasions. We discuss the circumstances in which this design can be used. The re-randomisation design will give asymptotically unbiased estimates of treatment effect and correct type I error rates under the following conditions: (a) patients are only re-randomised after the follow-up period from their previous randomisation is complete; (b) randomisations for the same patient are performed independently; and (c) the treatment effect is constant across all randomisations. Provided the analysis accounts for correlation between observations from the same patient, this design will typically have higher power than a parallel group trial with an equivalent number of observations. If used appropriately, the re-randomisation design can increase the recruitment rate for clinical trials while still providing an unbiased estimate of treatment effect and correct type I error rates. In many situations, it can increase the power compared to a parallel group design with an equivalent number of observations.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 2%
Unknown 48 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 35%
Student > Master 6 12%
Student > Ph. D. Student 4 8%
Other 4 8%
Professor 3 6%
Other 7 14%
Unknown 8 16%
Readers by discipline Count As %
Medicine and Dentistry 18 37%
Mathematics 6 12%
Social Sciences 2 4%
Unspecified 2 4%
Nursing and Health Professions 1 2%
Other 6 12%
Unknown 14 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 December 2019.
All research outputs
#2,931,584
of 24,312,464 outputs
Outputs from BMC Medical Research Methodology
#450
of 2,158 outputs
Outputs of similar age
#41,288
of 290,487 outputs
Outputs of similar age from BMC Medical Research Methodology
#5
of 21 outputs
Altmetric has tracked 24,312,464 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,158 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one has done well, scoring higher than 79% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 290,487 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.