Literature DB >> 22980714

Using best-worst scaling in horizon scanning for hepatocellular carcinoma technologies.

Gisselle Gallego1, John F P Bridges, Terry Flynn, Barri M Blauvelt, Louis W Niessen.   

Abstract

OBJECTIVES: There is a growing need for efficient procedures for identification of emerging technologies by horizon scanning systems. We demonstrate the value of best-worst scaling (BWS) in exploring clinicians' views on emerging technologies that will impact outcomes in hepatocellular carcinoma (HCC) in the next 5 to 10 years.
METHODS: Clinicians in Asia, Europe, and the United States were surveyed and their views about eleven emerging technologies relevant to HCC were explored using BWS (case 1). This involved systematically presenting respondents with subsets of five technologies and asking them to identify those that will have the most and least impact on HCC within 5 to 10 years. Statistical analysis was based on sequential best-worst and analyzed using conditional logistic regression.
RESULTS: A total of 120 clinicians uniformly distributed across ten countries completed the survey (37 percent response rate). Respondents were predominately hepatologist (41 percent) who focused on HCC (65 percent) and had national influence in this field (39 percent). Respondents viewed molecular targeted therapy (p < .001) and early detection of HCC (p < .001) as having most potential, while improved surgical techniques (p < .001) and biopsy free HCC diagnostics (p < .001) were viewed upon negatively.
CONCLUSIONS: We demonstrate that BWS could be an important research tool to facilitate horizon scanning and HTA more broadly. Our research demonstrates the value of including clinicians' preferences as a source of data in horizon scanning, but such methods could be used to incorporate the opinions of a broad array of stakeholders, including those in advocacy and public policy.

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Mesh:

Year:  2012        PMID: 22980714     DOI: 10.1017/S026646231200027X

Source DB:  PubMed          Journal:  Int J Technol Assess Health Care        ISSN: 0266-4623            Impact factor:   2.188


  16 in total

1.  A best-worst scaling experiment to prioritize caregiver concerns about ADHD medication for children.

Authors:  Melissa Ross; John F P Bridges; Xinyi Ng; Lauren D Wagner; Emily Frosch; Gloria Reeves; Susan dosReis
Journal:  Psychiatr Serv       Date:  2014-11-17       Impact factor: 3.084

2.  A comparison of two experimental design approaches in applying conjoint analysis in patient-centered outcomes research: a randomized trial.

Authors:  Elizabeth T Kinter; Thomas J Prior; Christopher I Carswell; John F P Bridges
Journal:  Patient       Date:  2012       Impact factor: 3.883

3.  Using Best-Worst Scaling to Understand Patient Priorities: A Case Example of Papanicolaou Tests for Homeless Women.

Authors:  Eve Wittenberg; Monica Bharel; John F P Bridges; Zachary Ward; Linda Weinreb
Journal:  Ann Fam Med       Date:  2016-07       Impact factor: 5.166

4.  Caregiver preferences for emerging duchenne muscular dystrophy treatments: a comparison of best-worst scaling and conjoint analysis.

Authors:  Ilene L Hollin; Holly L Peay; John F P Bridges
Journal:  Patient       Date:  2015-02       Impact factor: 3.883

5.  Prioritizing Parental Worry Associated with Duchenne Muscular Dystrophy Using Best-Worst Scaling.

Authors:  Holly Landrum Peay; I L Hollin; J F P Bridges
Journal:  J Genet Couns       Date:  2015-08-21       Impact factor: 2.537

6.  Factors impacting physicians' decisions to prevent variceal hemorrhage.

Authors:  Kathleen Yan; John F P Bridges; Salvador Augustin; Loren Laine; Guadalupe Garcia-Tsao; Liana Fraenkel
Journal:  BMC Gastroenterol       Date:  2015-05-02       Impact factor: 3.067

Review 7.  Using Best-Worst Scaling to Investigate Preferences in Health Care.

Authors:  Kei Long Cheung; Ben F M Wijnen; Ilene L Hollin; Ellen M Janssen; John F Bridges; Silvia M A A Evers; Mickael Hiligsmann
Journal:  Pharmacoeconomics       Date:  2016-12       Impact factor: 4.981

8.  A best-worst scaling experiment to identify patient-centered claims-based outcomes for evaluation of pediatric antipsychotic monitoring programs.

Authors:  Thomas I Mackie; Katherine M Kovacs; Cassandra Simmel; Stephen Crystal; Sheree Neese-Todd; Ayse Akincigil
Journal:  Health Serv Res       Date:  2020-12-28       Impact factor: 3.402

9.  Variation in physician recommendations, knowledge and perceived roles regarding provision of end-of-life care.

Authors:  Chetna Malhotra; Noreen Chan; Jamie Zhou; Hannah B Dalager; Eric Finkelstein
Journal:  BMC Palliat Care       Date:  2015-10-26       Impact factor: 3.234

10.  Experimental measurement of preferences in health and healthcare using best-worst scaling: an overview.

Authors:  Axel C Mühlbacher; Anika Kaczynski; Peter Zweifel; F Reed Johnson
Journal:  Health Econ Rev       Date:  2016-01-08
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