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Attention Score in Context
| Title |
Practical considerations for sensitivity analysis after multiple imputation applied to epidemiological studies with incomplete data
|
|---|---|
| Published in |
BMC Medical Research Methodology, June 2012
|
| DOI | 10.1186/1471-2288-12-73 |
| Pubmed ID | |
| Authors |
Vanina Héraud-Bousquet, Christine Larsen, James Carpenter, Jean-Claude Desenclos, Yann Le Strat |
| Abstract |
Multiple Imputation as usually implemented assumes that data are Missing At Random (MAR), meaning that the underlying missing data mechanism, given the observed data, is independent of the unobserved data. To explore the sensitivity of the inferences to departures from the MAR assumption, we applied the method proposed by Carpenter et al. (2007).This approach aims to approximate inferences under a Missing Not At random (MNAR) mechanism by reweighting estimates obtained after multiple imputation where the weights depend on the assumed degree of departure from the MAR assumption. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 2% |
| United States | 2 | 2% |
| Sweden | 1 | <1% |
| Netherlands | 1 | <1% |
| Canada | 1 | <1% |
| France | 1 | <1% |
| Unknown | 108 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 28 | 24% |
| Student > Ph. D. Student | 27 | 23% |
| Student > Doctoral Student | 12 | 10% |
| Professor > Associate Professor | 8 | 7% |
| Student > Master | 7 | 6% |
| Other | 18 | 16% |
| Unknown | 16 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 33 | 28% |
| Mathematics | 14 | 12% |
| Agricultural and Biological Sciences | 10 | 9% |
| Nursing and Health Professions | 7 | 6% |
| Psychology | 6 | 5% |
| Other | 21 | 18% |
| Unknown | 25 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 01 January 2015.
All research outputs
#7,171,179
of 22,669,724 outputs
Outputs from BMC Medical Research Methodology
#1,063
of 2,000 outputs
Outputs of similar age
#52,834
of 166,791 outputs
Outputs of similar age from BMC Medical Research Methodology
#9
of 26 outputs
Altmetric has tracked 22,669,724 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 2,000 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one is in the 46th percentile – i.e., 46% 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 166,791 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 67% of its contemporaries.
We're also able to compare this research output to 26 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 65% of its contemporaries.