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A preference-based item response theory model to measure health: concept and mathematics of the multi-attribute preference response model

Overview of attention for article published in BMC Medical Research Methodology, June 2018
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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 (78th percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

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Title
A preference-based item response theory model to measure health: concept and mathematics of the multi-attribute preference response model
Published in
BMC Medical Research Methodology, June 2018
DOI 10.1186/s12874-018-0516-8
Pubmed ID
Authors

Catharina G. M. Groothuis-Oudshoorn, Edwin R. van den Heuvel, Paul F. M. Krabbe

Abstract

A new patient-reported health measurement model has been developed to quantify descriptions of health states. Known as the multi-attribute preference response (MAPR) model, it is based on item response theory. The response task in the MAPR is for a patient to judge whether hypothetical health-state descriptions are better or worse than his/her own health status. In its most simple form MAPR is a Rasch model where for each respondent on the same unidimensional health scale values are estimated of their own health status and values of the hypothetical comparator health states. These values reflect the quality or severity of the health states. Alternatively, the respondents are offered health-state descriptions that are based on a classification system (e.g., multi-attribute) with a fixed number of health attributes, each with a limited number of levels. In the latter variant, the weights of the levels of the attributes in the descriptive system, which represents the range of the health states, are estimated. The results of a small empirical study are presented to illustrate the procedures of the MAPR model and possible extensions of the model are discussed. The small study that we conducted to illustrate the procedure and results of our proposed method to measure the quality of health states and patients' own health status showed confirming results. This paper introduces the typical MAPR model and shows how it extends the basic Rasch model with a regression function for the attributes of the health-state classification system.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 15%
Student > Doctoral Student 5 12%
Lecturer 3 7%
Researcher 2 5%
Student > Ph. D. Student 2 5%
Other 4 10%
Unknown 19 46%
Readers by discipline Count As %
Medicine and Dentistry 8 20%
Nursing and Health Professions 3 7%
Engineering 2 5%
Social Sciences 2 5%
Psychology 2 5%
Other 5 12%
Unknown 19 46%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 23 June 2018.
All research outputs
#3,590,034
of 23,092,602 outputs
Outputs from BMC Medical Research Methodology
#539
of 2,035 outputs
Outputs of similar age
#69,872
of 328,678 outputs
Outputs of similar age from BMC Medical Research Methodology
#16
of 46 outputs
Altmetric has tracked 23,092,602 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,035 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has gotten more attention than average, scoring higher than 73% 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 328,678 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 78% of its contemporaries.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.