Evidence map›Paper›PMID 30906968›Full record

ArticleThe patient2019

A Guide to Measuring and Interpreting Attribute Importance.

Juan Marcos Gonzalez

Abstract read
PubMed Publisher
In one paragraph

Article in The patient, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
79citing papers in PubMed, 2 pooled it
–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

79 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  3. Trial
  4. Do preferences differ based on respondent experience of a health issue and its treatment? A case study using a public health intervention.The European journal of health economics : HEPAC : health economics in prevention and care · 2023
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  16. Valuing health and wellbeing using discrete choice experiment: exploring feasibility, design effect and international preference similarity.The European journal of health economics : HEPAC : health economics in prevention and care · 2026
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19 more citing papers are in PubMed but not listed here.

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

1 author.

Juan Marcos GonzalezDuke University, 200 Morris St, Durham, NC, 27701, USA. jm.gonzalez@duke.edu.ORCID 0000-0002-5386-0907

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Stated-preference (SP) methods, such as discrete-choice experiments (DCE) and best-worst scaling (BWS), have increasingly been used to measure preferences for attributes of medical interventions. Preference information is commonly characterized using attribute importance. However, attribute importance measures  can vary in value and interpretation depending on the method used to elicit preferences, the specific context of the questions, and the approach used to normalize attribute effects. This variation complicates the interpretation of preference results and the comparability of results across subgroups in a sample. This article highlights the potential consequences of ignoring variations in attribute importance measures, and makes the case for reporting more clearly how these measures are obtained and calculated. Transparency in the calculations can clarify what conclusions are supported by the results, and help make more accurate and meaningful comparisons across subsamples.

Indexed as

Choice BehaviorDelivery of Health CarePatient PreferenceAlgorithmsHumansResearch Design

Identifiers

What OpenQuestion holds

Textmetadata
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.