↓ Skip to main content

Subgroup analyses in confirmatory clinical trials: time to be specific about their purposes

Overview of attention for article published in BMC Medical Research Methodology, February 2016
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
5 X users

Citations

dimensions_citation
65 Dimensions

Readers on

mendeley
98 Mendeley
citeulike
2 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Subgroup analyses in confirmatory clinical trials: time to be specific about their purposes
Published in
BMC Medical Research Methodology, February 2016
DOI 10.1186/s12874-016-0122-6
Pubmed ID
Authors

Julien Tanniou, Ingeborg van der Tweel, Steven Teerenstra, Kit C. B. Roes

Abstract

It is well recognized that treatment effects may not be homogeneous across the study population. Subgroup analyses constitute a fundamental step in the assessment of evidence from confirmatory (Phase III) clinical trials, where conclusions for the overall study population might not hold. Subgroup analyses can have different and distinct purposes, requiring specific design and analysis solutions. It is relevant to evaluate methodological developments in subgroup analyses against these purposes to guide health care professionals and regulators as well as to identify gaps in current methodology. We defined four purposes for subgroup analyses: (1) Investigate the consistency of treatment effects across subgroups of clinical importance, (2) Explore the treatment effect across different subgroups within an overall non-significant trial, (3) Evaluate safety profiles limited to one or a few subgroup(s), (4) Establish efficacy in the targeted subgroup when included in a confirmatory testing strategy of a single trial. We reviewed the methodology in line with this "purpose-based" framework. The review covered papers published between January 2005 and April 2015 and aimed to classify them in none, one or more of the aforementioned purposes. In total 1857 potentially eligible papers were identified. Forty-eight papers were selected and 20 additional relevant papers were identified from their references, leading to 68 papers in total. Nineteen were dedicated to purpose 1, 16 to purpose 4, one to purpose 2 and none to purpose 3. Seven papers were dedicated to more than one purpose, the 25 remaining could not be classified unambiguously. Purposes of the methods were often not specifically indicated, methods for subgroup analysis for safety purposes were almost absent and a multitude of diverse methods were developed for purpose (1). It is important that researchers developing methodology for subgroup analysis explicitly clarify the objectives of their methods in terms that can be understood from a patient's, health care provider's and/or regulator's perspective. A clear operational definition for consistency of treatment effects across subgroups is lacking, but is needed to improve the usability of subgroup analyses in this setting. Finally, methods to particularly explore benefit-risk systematically across subgroups need more research.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 1%
Unknown 97 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 19%
Student > Ph. D. Student 18 18%
Other 8 8%
Student > Master 7 7%
Student > Doctoral Student 4 4%
Other 19 19%
Unknown 23 23%
Readers by discipline Count As %
Medicine and Dentistry 13 13%
Mathematics 10 10%
Economics, Econometrics and Finance 6 6%
Computer Science 5 5%
Pharmacology, Toxicology and Pharmaceutical Science 5 5%
Other 25 26%
Unknown 34 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 03 March 2016.
All research outputs
#14,217,596
of 24,274,366 outputs
Outputs from BMC Medical Research Methodology
#1,342
of 2,155 outputs
Outputs of similar age
#143,778
of 302,665 outputs
Outputs of similar age from BMC Medical Research Methodology
#19
of 34 outputs
Altmetric has tracked 24,274,366 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,155 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
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 302,665 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.