Evidence map›Paper›PMID 40610706›Full record

ReviewThe patient2025

Developing Tools for the Efficient Design of Health Preference Studies: Taxonomy of Attributes and Prototype of an Attribute Library.

Norah L Crossnohere, Jonah Golder, Esther W de Bekker-Grob, Juan Marcos Gonzalez Sepulveda, Kert Gunasekaran, Alissa Hanna, Bennett Levitan, Barry Liden, Deborah Marshall, Christine Poulos and 2 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in The patient, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Norah L CrossnohereDivision of General Internal Medicine, The Ohio State University College of Medicine, Columbus, OH, USA.
Jonah GolderMedical Device Innovation Consortium, Arlington, VA, USA. jgolder@mdic.org.ORCID 0009-0004-9609-8993
Esther W de Bekker-GrobErasmus School of Health Policy and Management, Erasmus University, Rotterdam, The Netherlands.
Juan Marcos Gonzalez SepulvedaDuke Clinical Research Institute, Duke University, Durham, NC, USA.
Kert GunasekaranMedical Device Innovation Consortium, Arlington, VA, USA.
Alissa HannaEdwards Lifesciences, Irvine, CA, USA.
Bennett LevitanJohnson and Johnson, Titusville, NJ, USA.
Barry LidenSchaeffer Center for Health Policy and Economics, University of Southern California, Washington, DC, USA.
Deborah MarshallCumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Christine PoulosRTI Health Solutions, Research Triangle Park, NC, USA.
Shelby D ReedDuke Clinical Research Institute, Duke University, Durham, NC, USA.
Ellen M JanssenJohnson and Johnson, Titusville, NJ, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Preference information describes the relative desirability or acceptability of specified alternatives that differ across health states, interventions, or services. Studies that generate preference information are being designed to support patient-centered decision making across all stages of the medical product lifecycle, as well as in healthcare more generally. Ensuring high-quality preference research with the potential for impact requires transparent and thoughtful study design, a core aspect of which often includes the development of attributes. Good practices for attribute development in preference studies have started to emerge and demonstrate that developing attributes requires substantial time and effort. Resources to more easily and systematically identify potentially relevant attributes may support the accessibility, interoperability, and reusability of attributes, in turn improving the efficiency of preference study design and comparability of findings across studies. In this paper, we first describe the need for and potential benefit of tools that promote the purposeful re-use of attributes for preference studies. We next present a taxonomy for categorizing and describing attributes that could be applied to facilitate their identification. Finally, we apply this taxonomy to a prototype "attribute library," developed as a part of a Medical Device Innovation Consortium work group, to demonstrate the potential value of these resources to support the preference research community.

Indexed as

Patient PreferenceResearch DesignDecision MakingHumansPatient-Centered Care

Identifiers

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.