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The use of complete-case and multiple imputation-based analyses in molecular epidemiology studies that assess interaction effects

Overview of attention for article published in Epidemiologic Perspectives & Innovations, October 2011
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Title
The use of complete-case and multiple imputation-based analyses in molecular epidemiology studies that assess interaction effects
Published in
Epidemiologic Perspectives & Innovations, October 2011
DOI 10.1186/1742-5573-8-5
Pubmed ID
Authors

Manisha Desai, Denise A Esserman, Marilie D Gammon, Mary B Terry

Abstract

In molecular epidemiology studies biospecimen data are collected, often with the purpose of evaluating the synergistic role between a biomarker and another feature on an outcome. Typically, biomarker data are collected on only a proportion of subjects eligible for study, leading to a missing data problem. Missing data methods, however, are not customarily incorporated into analyses. Instead, complete-case (CC) analyses are performed, which can result in biased and inefficient estimates.

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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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 8%
Unknown 24 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 27%
Student > Master 4 15%
Student > Doctoral Student 2 8%
Student > Bachelor 1 4%
Professor 1 4%
Other 4 15%
Unknown 7 27%
Readers by discipline Count As %
Medicine and Dentistry 8 31%
Agricultural and Biological Sciences 4 15%
Mathematics 2 8%
Environmental Science 1 4%
Nursing and Health Professions 1 4%
Other 2 8%
Unknown 8 31%
Attention Score in Context

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 13 October 2011.
All research outputs
#18,297,449
of 22,653,392 outputs
Outputs from Epidemiologic Perspectives & Innovations
#31
of 36 outputs
Outputs of similar age
#111,273
of 133,853 outputs
Outputs of similar age from Epidemiologic Perspectives & Innovations
#1
of 1 outputs
Altmetric has tracked 22,653,392 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 36 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.9. This one scored the same or higher as 5 of them.
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 133,853 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them