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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Network meta-analysis of multiple outcome measures accounting for borrowing of information across outcomes
|
|---|---|
| Published in |
BMC Medical Research Methodology, July 2014
|
| DOI | 10.1186/1471-2288-14-92 |
| Pubmed ID | |
| Authors |
Felix A Achana, Nicola J Cooper, Sylwia Bujkiewicz, Stephanie J Hubbard, Denise Kendrick, David R Jones, Alex J Sutton |
| Abstract |
Network meta-analysis (NMA) enables simultaneous comparison of multiple treatments while preserving randomisation. When summarising evidence to inform an economic evaluation, it is important that the analysis accurately reflects the dependency structure within the data, as correlations between outcomes may have implication for estimating the net benefit associated with treatment. A multivariate NMA offers a framework for evaluating multiple treatments across multiple outcome measures while accounting for the correlation structure between outcomes. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 40% |
| Canada | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 1% |
| Netherlands | 1 | 1% |
| United States | 1 | 1% |
| Unknown | 68 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 20% |
| Researcher | 13 | 18% |
| Student > Doctoral Student | 9 | 13% |
| Student > Master | 7 | 10% |
| Professor > Associate Professor | 4 | 6% |
| Other | 15 | 21% |
| Unknown | 9 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 22 | 31% |
| Mathematics | 12 | 17% |
| Economics, Econometrics and Finance | 4 | 6% |
| Computer Science | 3 | 4% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 4% |
| Other | 9 | 13% |
| Unknown | 18 | 25% |
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 02 November 2021.
All research outputs
#2,619,080
of 22,919,505 outputs
Outputs from BMC Medical Research Methodology
#408
of 2,025 outputs
Outputs of similar age
#27,351
of 228,881 outputs
Outputs of similar age from BMC Medical Research Methodology
#4
of 22 outputs
Altmetric has tracked 22,919,505 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,025 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.1. 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 228,881 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 87% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.