ArticleEuropean journal of human genetics : EJHG2013
Eliciting preferences for priority setting in genetic testing: a pilot study comparing best-worst scaling and discrete-choice experiments.
Article in European journal of human genetics : EJHG, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 4 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Approaches to Incorporation of Preferences into Health Economic Models of Genomic Medicine: A Critical Interpretive Synthesis and Conceptual Framework.Applied health economics and health policy · 2025Pooled it
- A Systematic Review of Discrete Choice Experiments and Conjoint Analysis on Genetic Testing.The patient · 2022Pooled it
- Pooled it
- Using Best-Worst Scaling to Investigate Preferences in Health Care.PharmacoEconomics · 2016Pooled it
- Comparing Outcomes of a Discrete Choice Experiment and Case 2 Best-Worst Scaling: An Application to Neuromuscular Disease Treatment.The patient · 2023Trial
- Is best-worst scaling suitable for patient preference elicitation? A nationwide representative comparison with discrete choice experiments among patients with type 2 diabetes in China.BMJ public health · 2026Article
- Discrete Choice Experiment Versus Best-Worst Scaling: An Empirical Comparison in Eliciting Young People's Preferences for Web-Based Mental Health Interventions.The patient · 2025Article
- An Empirical Comparison of Discrete Choice Experiment and Best-Worst Scaling to Estimate Patient Preferences in Infertility Treatment in China.Patient preference and adherence · 2025Article
- Residents' Preferences for Inclusive Commercial Health Insurance: A Comparative Study of Discrete Choice Experiment and Best-Worst Scaling in Heilongjiang Province, China.Patient preference and adherence · 2025Article
- Comparing the Self-Reported Acceptability of Discrete Choice Experiment and Best-Worst Scaling: An Empirical Study in Patients with Type 2 Diabetes Mellitus.Patient preference and adherence · 2024Article
- The Perspective of Patients and Health Professionals on the Prioritization of Assisted Reproductive Techniques. The PRIOFER Study.Patient preference and adherence · 2023Article
- A mainstreaming oncogenomics model: improving the identification of Lynch syndrome.Frontiers in oncology · 2023Article
- Development of a Patient-Centered Preference Tool for Patients With Hematologic Malignancies: Protocol for a Mixed Methods Study.JMIR research protocols · 2022Article
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- Outcome preferences of older people with multiple chronic conditions and hypertension: a cross-sectional survey using best-worst scaling.Health and quality of life outcomes · 2019Article
- Challenges to informed consent for exome sequencing: A best-worst scaling experiment.Journal of genetic counseling · 2019Article
- Towards establishing consistency in triage in a tertiary specialty.European journal of human genetics : EJHG · 2019Article
- How psychological distance of a study sample in discrete choice experiments affects preference measurement: a colorectal cancer screening case study.Patient preference and adherence · 2019Article
- The value of genetic testing: beyond clinical utility.Genetics in medicine : official journal of the American College of Medical Genetics · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Given the increasing number of genetic tests available, decisions have to be made on how to allocate limited health-care resources to them. Different criteria have been proposed to guide priority setting. However, their relative importance is unclear. Discrete-choice experiments (DCEs) and best-worst scaling experiments (BWSs) are methods used to identify and weight various criteria that influence orders of priority. This study tests whether these preference eliciting techniques can be used for prioritising genetic tests and compares the empirical findings resulting from these two approaches. Pilot DCE and BWS questionnaires were developed for the same criteria: prevalence, severity, clinical utility, alternatives to genetic testing available, infrastructure for testing and care established, and urgency of care. Interview-style experiments were carried out among different genetics professionals (mainly clinical geneticists, researchers and biologists). A total of 31 respondents completed the DCE and 26 completed the BWS experiment. Weights for the levels of the six attributes were estimated by conditional logit models. Although the results derived from the DCE and BWS experiments differed in detail, we found similar valuation patterns in the DCE and BWS experiments. The respondents attached greatest value to tests with high clinical utility (defined by the availability of treatments that reduce mortality and morbidity) and to testing for highly prevalent conditions. The findings from this study exemplify how decision makers can use quantitative preference eliciting methods to measure aggregated preferences in order to prioritise alternative clinical interventions. Further research is necessary to confirm the survey results.
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