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Joint modelling rationale for chained equations

Overview of attention for article published in BMC Medical Research Methodology, February 2014
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Title
Joint modelling rationale for chained equations
Published in
BMC Medical Research Methodology, February 2014
DOI 10.1186/1471-2288-14-28
Pubmed ID
Authors

Rachael A Hughes, Ian R White, Shaun R Seaman, James R Carpenter, Kate Tilling, Jonathan AC Sterne

Abstract

Chained equations imputation is widely used in medical research. It uses a set of conditional models, so is more flexible than joint modelling imputation for the imputation of different types of variables (e.g. binary, ordinal or unordered categorical). However, chained equations imputation does not correspond to drawing from a joint distribution when the conditional models are incompatible. Concurrently with our work, other authors have shown the equivalence of the two imputation methods in finite samples.

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The data shown below were collected from the profiles of 3 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 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
United States 1 2%
Unknown 54 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 41%
Researcher 8 14%
Student > Master 7 13%
Professor > Associate Professor 3 5%
Student > Doctoral Student 2 4%
Other 5 9%
Unknown 8 14%
Readers by discipline Count As %
Mathematics 16 29%
Medicine and Dentistry 11 20%
Economics, Econometrics and Finance 3 5%
Social Sciences 3 5%
Psychology 3 5%
Other 8 14%
Unknown 12 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 03 July 2018.
All research outputs
#14,775,080
of 22,745,803 outputs
Outputs from BMC Medical Research Methodology
#1,439
of 2,005 outputs
Outputs of similar age
#128,702
of 224,442 outputs
Outputs of similar age from BMC Medical Research Methodology
#28
of 33 outputs
Altmetric has tracked 22,745,803 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,005 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 25th percentile – i.e., 25% of its peers scored the same or lower than it.
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We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.