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When and how should multiple imputation be used for handling missing data in randomised clinical trials – a practical guide with flowcharts

Overview of attention for article published in BMC Medical Research Methodology, December 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#40 of 2,000)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
policy
1 policy source
twitter
89 tweeters
facebook
1 Facebook page
q&a
1 Q&A thread

Citations

dimensions_citation
949 Dimensions

Readers on

mendeley
1362 Mendeley
citeulike
1 CiteULike
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Title
When and how should multiple imputation be used for handling missing data in randomised clinical trials – a practical guide with flowcharts
Published in
BMC Medical Research Methodology, December 2017
DOI 10.1186/s12874-017-0442-1
Pubmed ID
Authors

Janus Christian Jakobsen, Christian Gluud, Jørn Wetterslev, Per Winkel

Abstract

Missing data may seriously compromise inferences from randomised clinical trials, especially if missing data are not handled appropriately. The potential bias due to missing data depends on the mechanism causing the data to be missing, and the analytical methods applied to amend the missingness. Therefore, the analysis of trial data with missing values requires careful planning and attention. The authors had several meetings and discussions considering optimal ways of handling missing data to minimise the bias potential. We also searched PubMed (key words: missing data; randomi*; statistical analysis) and reference lists of known studies for papers (theoretical papers; empirical studies; simulation studies; etc.) on how to deal with missing data when analysing randomised clinical trials. Handling missing data is an important, yet difficult and complex task when analysing results of randomised clinical trials. We consider how to optimise the handling of missing data during the planning stage of a randomised clinical trial and recommend analytical approaches which may prevent bias caused by unavoidable missing data. We consider the strengths and limitations of using of best-worst and worst-best sensitivity analyses, multiple imputation, and full information maximum likelihood. We also present practical flowcharts on how to deal with missing data and an overview of the steps that always need to be considered during the analysis stage of a trial. We present a practical guide and flowcharts describing when and how multiple imputation should be used to handle missing data in randomised clinical.

Twitter Demographics

The data shown below were collected from the profiles of 89 tweeters 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 1,362 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1362 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 300 22%
Student > Master 195 14%
Researcher 174 13%
Student > Bachelor 102 7%
Student > Doctoral Student 87 6%
Other 176 13%
Unknown 328 24%
Readers by discipline Count As %
Medicine and Dentistry 232 17%
Psychology 159 12%
Nursing and Health Professions 82 6%
Social Sciences 82 6%
Mathematics 44 3%
Other 342 25%
Unknown 421 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 74. 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 28 May 2022.
All research outputs
#482,986
of 22,651,245 outputs
Outputs from BMC Medical Research Methodology
#40
of 2,000 outputs
Outputs of similar age
#12,424
of 438,026 outputs
Outputs of similar age from BMC Medical Research Methodology
#5
of 46 outputs
Altmetric has tracked 22,651,245 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
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 has done particularly well, scoring higher than 98% 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 438,026 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.