Evidence map›Paper›PMID 27695386›Full record

ReviewJournal of substance use2016

Developing attributes for discrete choice experiments in health: a systematic literature review and case study of alcohol misuse interventions.

Timea Mariann Helter, Christian Ernst Heinrich Boehler

Abstract readReview
In one paragraph

Review in Journal of substance use, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
67citing papers in PubMed, 3 pooled it
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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

67 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  3. Preferences for Genetic Testing to Predict the Risk of Developing Hereditary Cancer: A Systematic Review of Discrete Choice Experiments.Medical decision making : an international journal of the Society for Medical Decision Making · 2024
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7 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

2 authors.

Timea Mariann HelterHealth Economics Research Group, Brunel University , Middlesex, UK.
Christian Ernst Heinrich BoehlerEuropean Commission, Joint Research Centre, Institute for Prospective Technological Studies , Seville, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Discrete choice experiments (DCEs) become increasingly popular to value outcomes for health economic studies and gradually gain acceptance as an input into policy decisions. Developing attributes is a key aspect for the design of DCEs, as their results may misguide decision-makers if they are based on an inappropriate set of attributes. However, the area lacks guidance, and current health-related DCE studies vary considerably in their methods of attribute development, with the consequent danger of providing an unreliable input for policy decisions. The aim of this article is to inform the progress toward a more systematic approach to attribute development for DCE studies in health. A systematic review of the published health-related DCE literature was conducted to lay the foundations for a generic framework which was tested in a case study of alcohol misuse interventions. Four stages of a general attribute development process emerged: (i) raw data collection; (ii) data reduction; (iii) removing inappropriate attributes; and (iv) wording. The case study compared and contrasted a qualitative and mixed-methods approach for the development of attributes for DCEs in the area of alcohol misuse interventions. This article provides a reference point for the design of future DCE experiments in health.

Indexed as

Alcohol misusediscrete choice experimentssystematic review

Identifiers

PMID27695386
PMCPMC5022136

What OpenQuestion holds

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LicenceCC BY
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.