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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 2,282)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
policy
2 policy sources
twitter
97 X users
facebook
1 Facebook page
q&a
1 Q&A thread

Citations

dimensions_citation
1489 Dimensions

Readers on

mendeley
1685 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 97 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 1,685 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1685 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 341 20%
Student > Master 236 14%
Researcher 202 12%
Student > Bachelor 118 7%
Student > Doctoral Student 102 6%
Other 214 13%
Unknown 472 28%
Readers by discipline Count As %
Medicine and Dentistry 278 16%
Psychology 202 12%
Nursing and Health Professions 95 6%
Social Sciences 95 6%
Computer Science 47 3%
Other 393 23%
Unknown 575 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 81. 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 10 July 2023.
All research outputs
#525,263
of 25,402,889 outputs
Outputs from BMC Medical Research Methodology
#40
of 2,282 outputs
Outputs of similar age
#11,871
of 446,072 outputs
Outputs of similar age from BMC Medical Research Methodology
#4
of 46 outputs
Altmetric has tracked 25,402,889 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 2,282 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. 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 446,072 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 93% of its contemporaries.