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The development of CHAMP: a checklist for the appraisal of moderators and predictors

Overview of attention for article published in BMC Medical Research Methodology, December 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

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19 tweeters

Citations

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12 Dimensions

Readers on

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77 Mendeley
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Title
The development of CHAMP: a checklist for the appraisal of moderators and predictors
Published in
BMC Medical Research Methodology, December 2017
DOI 10.1186/s12874-017-0451-0
Pubmed ID
Authors

Ralph van Hoorn, Marcia Tummers, Andrew Booth, Ansgar Gerhardus, Eva Rehfuess, Daniel Hind, Patrick M. Bossuyt, Vivian Welch, Thomas P. A. Debray, Martin Underwood, Pim Cuijpers, Helena Kraemer, Gert Jan van der Wilt, Wietkse Kievit

Abstract

Personalized healthcare relies on the identification of factors explaining why individuals respond differently to the same intervention. Analyses identifying such factors, so called predictors and moderators, have their own set of assumptions and limitations which, when violated, can result in misleading claims, and incorrect actions. The aim of this study was to develop a checklist for critically appraising the results of predictor and moderator analyses by combining recommendations from published guidelines and experts in the field. Candidate criteria for the checklist were retrieved through systematic searches of the literature. These criteria were evaluated for appropriateness using a Delphi procedure. Two Delphi rounds yielded a pilot checklist, which was tested on a set of papers included in a systematic review on reinforced home-based palliative care. The results of the pilot informed a third Delphi round, which served to finalize the checklist. Forty-nine appraisal criteria were identified in the literature. Feedback was obtained from fourteen experts from (bio)statistics, epidemiology and other associated fields elicited via three Delphi rounds. Additional feedback from other researchers was collected in a pilot test. The final version of our checklist included seventeen criteria, covering the design (e.g. a priori plausibility), analysis (e.g. use of interaction tests) and results (e.g. complete reporting) of moderator and predictor analysis, together with the transferability of the results (e.g. clinical importance). There are criteria both for individual papers and for bodies of evidence. The proposed checklist can be used for critical appraisal of reported moderator and predictor effects, as assessed in randomized or non-randomized studies using individual participant or aggregate data. This checklist is accompanied by a user's guide to facilitate implementation. Its future use across a wide variety of research domains and study types will provide insights about its usability and feasibility.

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 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 77 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 17%
Student > Master 13 17%
Student > Ph. D. Student 12 16%
Student > Bachelor 6 8%
Professor > Associate Professor 4 5%
Other 14 18%
Unknown 15 19%
Readers by discipline Count As %
Medicine and Dentistry 16 21%
Psychology 10 13%
Nursing and Health Professions 6 8%
Social Sciences 5 6%
Pharmacology, Toxicology and Pharmaceutical Science 5 6%
Other 13 17%
Unknown 22 29%

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 August 2018.
All research outputs
#2,605,441
of 19,218,475 outputs
Outputs from BMC Medical Research Methodology
#446
of 1,734 outputs
Outputs of similar age
#76,295
of 426,427 outputs
Outputs of similar age from BMC Medical Research Methodology
#35
of 149 outputs
Altmetric has tracked 19,218,475 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 1,734 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.4. 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 426,427 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 149 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.