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Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines

Overview of attention for article published in BMC Medical Research Methodology, July 2009
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Citations

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255 Dimensions

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265 Mendeley
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2 CiteULike
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Title
Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines
Published in
BMC Medical Research Methodology, July 2009
DOI 10.1186/1471-2288-9-57
Pubmed ID
Authors

Andrea Marshall, Douglas G Altman, Roger L Holder, Patrick Royston

Abstract

Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. The multiply imputed datasets are each analysed using standard prognostic modelling techniques to obtain the estimates of interest. The estimates from each imputed dataset are then combined into one overall estimate and variance, incorporating both the within and between imputation variability. Rubin's rules for combining these multiply imputed estimates are based on asymptotic theory. The resulting combined estimates may be more accurate if the posterior distribution of the population parameter of interest is better approximated by the normal distribution. However, the normality assumption may not be appropriate for all the parameters of interest when analysing prognostic modelling studies, such as predicted survival probabilities and model performance measures.

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 265 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 2%
United Kingdom 5 2%
Canada 2 <1%
Sweden 1 <1%
Australia 1 <1%
Norway 1 <1%
Netherlands 1 <1%
Singapore 1 <1%
Denmark 1 <1%
Other 1 <1%
Unknown 246 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 72 27%
Researcher 50 19%
Student > Master 40 15%
Student > Doctoral Student 19 7%
Professor > Associate Professor 18 7%
Other 49 18%
Unknown 17 6%
Readers by discipline Count As %
Medicine and Dentistry 121 46%
Mathematics 28 11%
Psychology 18 7%
Social Sciences 12 5%
Computer Science 10 4%
Other 37 14%
Unknown 39 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 July 2018.
All research outputs
#2,509,305
of 13,801,769 outputs
Outputs from BMC Medical Research Methodology
#405
of 1,265 outputs
Outputs of similar age
#34,344
of 210,793 outputs
Outputs of similar age from BMC Medical Research Methodology
#18
of 83 outputs
Altmetric has tracked 13,801,769 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,265 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one has gotten more attention than average, scoring higher than 67% 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 210,793 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 83% of its contemporaries.
We're also able to compare this research output to 83 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.