↓ Skip to main content

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

Overview of attention for article published in Trials, August 2010
Altmetric Badge

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)

Citations

dimensions_citation
328 Dimensions

Readers on

mendeley
238 Mendeley
citeulike
7 CiteULike
Title
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
Authors

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

Abstract

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 18 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 238 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%
France 1 <1%
Germany 1 <1%
Portugal 1 <1%
Unknown 220 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 53 22%
Student > Ph. D. Student 42 18%
Professor > Associate Professor 23 10%
Student > Master 22 9%
Student > Doctoral Student 20 8%
Other 58 24%
Unknown 20 8%
Readers by discipline Count As %
Medicine and Dentistry 111 47%
Mathematics 18 8%
Agricultural and Biological Sciences 11 5%
Social Sciences 10 4%
Engineering 8 3%
Other 41 17%
Unknown 39 16%

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 01 October 2021.
All research outputs
#882,483
of 19,140,651 outputs
Outputs from Trials
#198
of 4,945 outputs
Outputs of similar age
#3,120
of 80,256 outputs
Outputs of similar age from Trials
#1
of 1 outputs
Altmetric has tracked 19,140,651 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 4,945 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one has done particularly well, scoring higher than 96% 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 80,256 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 96% 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