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www.common-metrics.org: a web application to estimate scores from different patient-reported outcome measures on a common scale

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
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

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8 X users

Citations

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17 Dimensions

Readers on

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65 Mendeley
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Title
www.common-metrics.org: a web application to estimate scores from different patient-reported outcome measures on a common scale
Published in
BMC Medical Research Methodology, October 2016
DOI 10.1186/s12874-016-0241-0
Pubmed ID
Authors

H. Felix Fischer, Matthias Rose

Abstract

Recently, a growing number of Item-Response Theory (IRT) models has been published, which allow estimation of a common latent variable from data derived by different Patient Reported Outcomes (PROs). When using data from different PROs, direct estimation of the latent variable has some advantages over the use of sum score conversion tables. It requires substantial proficiency in the field of psychometrics to fit such models using contemporary IRT software. We developed a web application ( http://www.common-metrics.org ), which allows estimation of latent variable scores more easily using IRT models calibrating different measures on instrument independent scales. Currently, the application allows estimation using six different IRT models for Depression, Anxiety, and Physical Function. Based on published item parameters, users of the application can directly estimate latent trait estimates using expected a posteriori (EAP) for sum scores as well as for specific response patterns, Bayes modal (MAP), Weighted likelihood estimation (WLE) and Maximum likelihood (ML) methods and under three different prior distributions. The obtained estimates can be downloaded and analyzed using standard statistical software. This application enhances the usability of IRT modeling for researchers by allowing comparison of the latent trait estimates over different PROs, such as the Patient Health Questionnaire Depression (PHQ-9) and Anxiety (GAD-7) scales, the Center of Epidemiologic Studies Depression Scale (CES-D), the Beck Depression Inventory (BDI), PROMIS Anxiety and Depression Short Forms and others. Advantages of this approach include comparability of data derived with different measures and tolerance against missing values. The validity of the underlying models needs to be investigated in the future.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 2%
Unknown 64 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 22%
Student > Ph. D. Student 8 12%
Student > Doctoral Student 8 12%
Student > Master 6 9%
Student > Bachelor 5 8%
Other 15 23%
Unknown 9 14%
Readers by discipline Count As %
Medicine and Dentistry 13 20%
Psychology 9 14%
Social Sciences 6 9%
Engineering 4 6%
Agricultural and Biological Sciences 3 5%
Other 17 26%
Unknown 13 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 30 June 2018.
All research outputs
#4,589,955
of 22,896,955 outputs
Outputs from BMC Medical Research Methodology
#728
of 2,024 outputs
Outputs of similar age
#77,221
of 315,882 outputs
Outputs of similar age from BMC Medical Research Methodology
#10
of 42 outputs
Altmetric has tracked 22,896,955 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,024 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.1. This one has gotten more attention than average, scoring higher than 64% 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 315,882 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 75% of its contemporaries.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.