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Handling trial participants with missing outcome data when conducting a meta-analysis: a systematic survey of proposed approaches

Overview of attention for article published in Systematic Reviews, July 2015
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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 (89th percentile)

Mentioned by

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27 tweeters

Citations

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37 Dimensions

Readers on

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47 Mendeley
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1 CiteULike
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Title
Handling trial participants with missing outcome data when conducting a meta-analysis: a systematic survey of proposed approaches
Published in
Systematic Reviews, July 2015
DOI 10.1186/s13643-015-0083-6
Pubmed ID
Authors

Elie A. Akl, Lara A. Kahale, Thomas Agoritsas, Romina Brignardello-Petersen, Jason W. Busse, Alonso Carrasco-Labra, Shanil Ebrahim, Bradley C. Johnston, Ignacio Neumann, Ivan Sola, Xin Sun, Per Vandvik, Yuqing Zhang, Pablo Alonso-Coello, Gordon Guyatt

Abstract

When potentially associated with the likelihood of outcome, missing participant data represents a serious potential source of bias in randomized trials. Authors of systematic reviews frequently face this problem when conducting meta-analyses. The objective of this study is to conduct a systematic survey of the relevant literature to identify proposed approaches for how systematic review authors should handle missing participant data when conducting a meta-analysis. We searched MEDLINE and the Cochrane Methodology register from inception to August 2014. We included papers that devoted at least two paragraphs to discuss a relevant approach for missing data. Five pairs of reviewers, working independently and in duplicate, selected relevant papers. One reviewer abstracted data from included papers and a second reviewer verified them. We summarized the results narratively. Of 9,138 identified citations, we included 11 eligible papers. Four proposed general approaches for handling dichotomous outcomes, and all recommended a complete case analysis as the primary analysis and additional sensitivity analyses using the following imputation methods: based on reasons for missingness (n = 3), relative to risk among followed up (n = 3), best-case scenario (n = 2), and worst-case scenario (n = 3). Three of these approaches suggested taking uncertainty into account. Two papers proposed general approaches for handling continuous outcomes, and both proposed a complete case analysis as the reference analysis and the following imputation methods as sensitivity analyses: based on reasons for missingness (n = 2), based on the mean observed in the same trial or other trials (n = 1), and based on informative missingness differences in means (n = 1). The remaining eligible papers did not propose general approaches but addressed specific statistical issues. All proposed approaches for handling missing participant data recommend conducting a complete case analysis for the primary analysis and some form of sensitivity analysis to evaluate robustness of results. Although these approaches require further testing, they may guide review authors in addressing missing participant data.

Twitter Demographics

The data shown below were collected from the profiles of 27 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 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 46 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 21%
Student > Ph. D. Student 9 19%
Student > Master 7 15%
Other 4 9%
Student > Doctoral Student 3 6%
Other 9 19%
Unknown 5 11%
Readers by discipline Count As %
Medicine and Dentistry 18 38%
Psychology 6 13%
Nursing and Health Professions 4 9%
Mathematics 4 9%
Agricultural and Biological Sciences 2 4%
Other 7 15%
Unknown 6 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 31 August 2020.
All research outputs
#1,502,978
of 17,356,510 outputs
Outputs from Systematic Reviews
#291
of 1,561 outputs
Outputs of similar age
#24,828
of 240,740 outputs
Outputs of similar age from Systematic Reviews
#1
of 3 outputs
Altmetric has tracked 17,356,510 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,561 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.7. This one has done well, scoring higher than 81% 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 240,740 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 89% of its contemporaries.
We're also able to compare this research output to 3 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