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Simulation-based estimation of mean and standard deviation for meta-analysis via Approximate Bayesian Computation (ABC)

Overview of attention for article published in BMC Medical Research Methodology, August 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)

Mentioned by

blogs
1 blog
twitter
2 tweeters

Citations

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16 Dimensions

Readers on

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25 Mendeley
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Title
Simulation-based estimation of mean and standard deviation for meta-analysis via Approximate Bayesian Computation (ABC)
Published in
BMC Medical Research Methodology, August 2015
DOI 10.1186/s12874-015-0055-5
Pubmed ID
Authors

Deukwoo Kwon, Isildinha M. Reis

Abstract

When conducting a meta-analysis of a continuous outcome, estimated means and standard deviations from the selected studies are required in order to obtain an overall estimate of the mean effect and its confidence interval. If these quantities are not directly reported in the publications, they must be estimated from other reported summary statistics, such as the median, the minimum, the maximum, and quartiles. We propose a simulation-based estimation approach using the Approximate Bayesian Computation (ABC) technique for estimating mean and standard deviation based on various sets of summary statistics found in published studies. We conduct a simulation study to compare the proposed ABC method with the existing methods of Hozo et al. (2005), Bland (2015), and Wan et al. (2014). In the estimation of the standard deviation, our ABC method performs better than the other methods when data are generated from skewed or heavy-tailed distributions. The corresponding average relative error (ARE) approaches zero as sample size increases. In data generated from the normal distribution, our ABC performs well. However, the Wan et al. method is best for estimating standard deviation under normal distribution. In the estimation of the mean, our ABC method is best regardless of assumed distribution. ABC is a flexible method for estimating the study-specific mean and standard deviation for meta-analysis, especially with underlying skewed or heavy-tailed distributions. The ABC method can be applied using other reported summary statistics such as the posterior mean and 95 % credible interval when Bayesian analysis has been employed.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
Unknown 24 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 20%
Researcher 4 16%
Student > Doctoral Student 3 12%
Other 2 8%
Professor 1 4%
Other 4 16%
Unknown 6 24%
Readers by discipline Count As %
Medicine and Dentistry 5 20%
Engineering 2 8%
Mathematics 2 8%
Agricultural and Biological Sciences 2 8%
Nursing and Health Professions 1 4%
Other 5 20%
Unknown 8 32%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 07 February 2020.
All research outputs
#2,890,190
of 17,814,645 outputs
Outputs from BMC Medical Research Methodology
#487
of 1,654 outputs
Outputs of similar age
#43,276
of 242,578 outputs
Outputs of similar age from BMC Medical Research Methodology
#1
of 1 outputs
Altmetric has tracked 17,814,645 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,654 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.9. This one has gotten more attention than average, scoring higher than 70% 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 242,578 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 82% of its contemporaries.
We're also able to compare this research output to 1 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