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

Publication ,  Journal Article
Crossnohere, NL; Golder, J; de Bekker-Grob, EW; Sepulveda, JMG; Gunasekaran, K; Hanna, A; Levitan, B; Liden, B; Marshall, D; Poulos, C ...
Published in: Patient
November 2025

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.

Duke Scholars

Published In

Patient

DOI

EISSN

1178-1661

Publication Date

November 2025

Volume

18

Issue

6

Start / End Page

585 / 596

Location

New Zealand

Related Subject Headings

  • Research Design
  • Patient-Centered Care
  • Patient Preference
  • Humans
  • Decision Making
  • 42 Health sciences
  • 32 Biomedical and clinical sciences
  • 11 Medical and Health Sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Crossnohere, N. L., Golder, J., de Bekker-Grob, E. W., Sepulveda, J. M. G., Gunasekaran, K., Hanna, A., … Janssen, E. M. (2025). Developing Tools for the Efficient Design of Health Preference Studies: Taxonomy of Attributes and Prototype of an Attribute Library. Patient, 18(6), 585–596. https://doi.org/10.1007/s40271-025-00751-9
Crossnohere, Norah L., Jonah Golder, Esther W. de Bekker-Grob, Juan Marcos Gonzalez Sepulveda, Kert Gunasekaran, Alissa Hanna, Bennett Levitan, et al. “Developing Tools for the Efficient Design of Health Preference Studies: Taxonomy of Attributes and Prototype of an Attribute Library.Patient 18, no. 6 (November 2025): 585–96. https://doi.org/10.1007/s40271-025-00751-9.
Crossnohere NL, Golder J, de Bekker-Grob EW, Sepulveda JMG, Gunasekaran K, Hanna A, et al. Developing Tools for the Efficient Design of Health Preference Studies: Taxonomy of Attributes and Prototype of an Attribute Library. Patient. 2025 Nov;18(6):585–96.
Crossnohere, Norah L., et al. “Developing Tools for the Efficient Design of Health Preference Studies: Taxonomy of Attributes and Prototype of an Attribute Library.Patient, vol. 18, no. 6, Nov. 2025, pp. 585–96. Pubmed, doi:10.1007/s40271-025-00751-9.
Crossnohere NL, Golder J, de Bekker-Grob EW, Sepulveda JMG, Gunasekaran K, Hanna A, Levitan B, Liden B, Marshall D, Poulos C, Reed SD, Janssen EM. Developing Tools for the Efficient Design of Health Preference Studies: Taxonomy of Attributes and Prototype of an Attribute Library. Patient. 2025 Nov;18(6):585–596.
Journal cover image

Published In

Patient

DOI

EISSN

1178-1661

Publication Date

November 2025

Volume

18

Issue

6

Start / End Page

585 / 596

Location

New Zealand

Related Subject Headings

  • Research Design
  • Patient-Centered Care
  • Patient Preference
  • Humans
  • Decision Making
  • 42 Health sciences
  • 32 Biomedical and clinical sciences
  • 11 Medical and Health Sciences