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Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal

Overview of attention for article published in Trials, August 2010
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)


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Readers on

220 Mendeley
7 CiteULike
Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal
Published in
Trials, August 2010
DOI 10.1186/1745-6215-11-85
Pubmed ID

David M Kent, Peter M Rothwell, John PA Ioannidis, Doug G Altman, Rodney A Hayward


Mounting evidence suggests that there is frequently considerable variation in the risk of the outcome of interest in clinical trial populations. These differences in risk will often cause clinically important heterogeneity in treatment effects (HTE) across the trial population, such that the balance between treatment risks and benefits may differ substantially between large identifiable patient subgroups; the "average" benefit observed in the summary result may even be non-representative of the treatment effect for a typical patient in the trial. Conventional subgroup analyses, which examine whether specific patient characteristics modify the effects of treatment, are usually unable to detect even large variations in treatment benefit (and harm) across risk groups because they do not account for the fact that patients have multiple characteristics simultaneously that affect the likelihood of treatment benefit. Based upon recent evidence on optimal statistical approaches to assessing HTE, we propose a framework that prioritizes the analysis and reporting of multivariate risk-based HTE and suggests that other subgroup analyses should be explicitly labeled either as primary subgroup analyses (well-motivated by prior evidence and intended to produce clinically actionable results) or secondary (exploratory) subgroup analyses (performed to inform future research). A standardized and transparent approach to HTE assessment and reporting could substantially improve clinical trial utility and interpretability.

Twitter Demographics

The data shown below were collected from the profiles of 19 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 220 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 12 5%
United Kingdom 3 1%
Portugal 2 <1%
Germany 1 <1%
France 1 <1%
Unknown 201 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 51 23%
Student > Ph. D. Student 39 18%
Student > Master 24 11%
Professor > Associate Professor 23 10%
Other 20 9%
Other 53 24%
Unknown 10 5%
Readers by discipline Count As %
Medicine and Dentistry 110 50%
Mathematics 18 8%
Agricultural and Biological Sciences 10 5%
Social Sciences 9 4%
Engineering 8 4%
Other 36 16%
Unknown 29 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 31. 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 13 November 2019.
All research outputs
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Outputs from Trials
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Outputs of similar age
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Altmetric has tracked 15,575,313 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,112 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has done particularly well, scoring higher than 95% of its peers.
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