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Multiple imputation for estimating hazard ratios and predictive abilities in case-cohort surveys

Overview of attention for article published in BMC Medical Research Methodology, March 2012
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Mentioned by

twitter
1 tweeter

Citations

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4 Dimensions

Readers on

mendeley
27 Mendeley
Title
Multiple imputation for estimating hazard ratios and predictive abilities in case-cohort surveys
Published in
BMC Medical Research Methodology, March 2012
DOI 10.1186/1471-2288-12-24
Pubmed ID
Authors

Helena Marti, Laure Carcaillon, Michel Chavance

Abstract

The weighted estimators generally used for analyzing case-cohort studies are not fully efficient and naive estimates of the predictive ability of a model from case-cohort data depend on the subcohort size. However, case-cohort studies represent a special type of incomplete data, and methods for analyzing incomplete data should be appropriate, in particular multiple imputation (MI).

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 7%
Germany 1 4%
Unknown 24 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 33%
Student > Ph. D. Student 6 22%
Student > Doctoral Student 4 15%
Student > Master 4 15%
Other 2 7%
Other 2 7%
Readers by discipline Count As %
Medicine and Dentistry 15 56%
Mathematics 4 15%
Nursing and Health Professions 2 7%
Psychology 2 7%
Business, Management and Accounting 1 4%
Other 2 7%
Unknown 1 4%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 12 March 2012.
All research outputs
#7,762,388
of 12,373,180 outputs
Outputs from BMC Medical Research Methodology
#778
of 1,095 outputs
Outputs of similar age
#63,905
of 116,578 outputs
Outputs of similar age from BMC Medical Research Methodology
#5
of 13 outputs
Altmetric has tracked 12,373,180 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,095 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.5. This one is in the 20th percentile – i.e., 20% 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 116,578 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
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 is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.