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Methods for network meta-analysis of continuous outcomes using individual patient data: a case study in acupuncture for chronic pain

Overview of attention for article published in BMC Medical Research Methodology, October 2016
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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 (81st percentile)

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14 tweeters
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3 Facebook pages

Citations

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8 Dimensions

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65 Mendeley
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Title
Methods for network meta-analysis of continuous outcomes using individual patient data: a case study in acupuncture for chronic pain
Published in
BMC Medical Research Methodology, October 2016
DOI 10.1186/s12874-016-0224-1
Pubmed ID
Authors

Pedro Saramago, Beth Woods, Helen Weatherly, Andrea Manca, Mark Sculpher, Kamran Khan, Andrew J. Vickers, Hugh MacPherson

Abstract

Network meta-analysis methods, which are an extension of the standard pair-wise synthesis framework, allow for the simultaneous comparison of multiple interventions and consideration of the entire body of evidence in a single statistical model. There are well-established advantages to using individual patient data to perform network meta-analysis and methods for network meta-analysis of individual patient data have already been developed for dichotomous and time-to-event data. This paper describes appropriate methods for the network meta-analysis of individual patient data on continuous outcomes. This paper introduces and describes network meta-analysis of individual patient data models for continuous outcomes using the analysis of covariance framework. Comparisons are made between this approach and change score and final score only approaches, which are frequently used and have been proposed in the methodological literature. A motivating example on the effectiveness of acupuncture for chronic pain is used to demonstrate the methods. Individual patient data on 28 randomised controlled trials were synthesised. Consistency of endpoints across the evidence base was obtained through standardisation and mapping exercises. Individual patient data availability avoided the use of non-baseline-adjusted models, allowing instead for analysis of covariance models to be applied and thus improving the precision of treatment effect estimates while adjusting for baseline imbalance. The network meta-analysis of individual patient data using the analysis of covariance approach is advocated to be the most appropriate modelling approach for network meta-analysis of continuous outcomes, particularly in the presence of baseline imbalance. Further methods developments are required to address the challenge of analysing aggregate level data in the presence of baseline imbalance.

Twitter Demographics

The data shown below were collected from the profiles of 14 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 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 65 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 13 20%
Researcher 10 15%
Other 8 12%
Student > Bachelor 7 11%
Student > Postgraduate 4 6%
Other 10 15%
Unknown 13 20%
Readers by discipline Count As %
Medicine and Dentistry 21 32%
Nursing and Health Professions 8 12%
Neuroscience 5 8%
Agricultural and Biological Sciences 4 6%
Economics, Econometrics and Finance 3 5%
Other 9 14%
Unknown 15 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 January 2017.
All research outputs
#2,436,290
of 17,814,645 outputs
Outputs from BMC Medical Research Methodology
#427
of 1,654 outputs
Outputs of similar age
#51,060
of 278,806 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 86th 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 74% 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 278,806 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 81% 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