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A test for reporting bias in trial networks: simulation and case studies

Overview of attention for article published in BMC Medical Research Methodology, September 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

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6 X users

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
47 Mendeley
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1 CiteULike
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Title
A test for reporting bias in trial networks: simulation and case studies
Published in
BMC Medical Research Methodology, September 2014
DOI 10.1186/1471-2288-14-112
Pubmed ID
Authors

Ludovic Trinquart, John PA Ioannidis, Gilles Chatellier, Philippe Ravaud

Abstract

Networks of trials assessing several treatment options available for the same condition are increasingly considered. Randomized trial evidence may be missing because of reporting bias. We propose a test for reporting bias in trial networks.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
New Zealand 1 2%
France 1 2%
Norway 1 2%
Unknown 44 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 28%
Student > Master 7 15%
Professor 5 11%
Student > Ph. D. Student 5 11%
Professor > Associate Professor 5 11%
Other 9 19%
Unknown 3 6%
Readers by discipline Count As %
Medicine and Dentistry 22 47%
Psychology 4 9%
Agricultural and Biological Sciences 3 6%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Linguistics 1 2%
Other 8 17%
Unknown 7 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 22 June 2022.
All research outputs
#8,102,927
of 24,312,464 outputs
Outputs from BMC Medical Research Methodology
#1,171
of 2,158 outputs
Outputs of similar age
#84,751
of 257,286 outputs
Outputs of similar age from BMC Medical Research Methodology
#6
of 13 outputs
Altmetric has tracked 24,312,464 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
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 is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
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 257,286 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.