Literature DB >> 27325322

An Empirical Comparison of Discrete Choice Experiment and Best-Worst Scaling to Estimate Stakeholders' Risk Tolerance for Hip Replacement Surgery.

Joris D van Dijk1, Catharina G M Groothuis-Oudshoorn2, Deborah A Marshall3, Maarten J IJzerman1.   

Abstract

BACKGROUND: Previous studies have been inconclusive regarding the validity and reliability of preference elicitation methods.
OBJECTIVE: The aim of this study was to compare the metrics obtained from a discrete choice experiment (DCE) and profile-case best-worst scaling (BWS) with respect to hip replacement.
METHODS: We surveyed the general US population of men aged 45 to 65 years, and potentially eligible for hip replacement surgery. The survey included sociodemographic questions, eight DCE questions, and twelve BWS questions. Attributes were the probability of a first and second revision, pain relief, ability to participate in sports and perform daily activities, and length of hospital stay. Conditional logit analysis was used to estimate attribute weights, level preferences, and the maximum acceptable risk (MAR) for undergoing revision surgery in six hypothetical treatment scenarios with different attribute levels.
RESULTS: A total of 429 (96%) respondents were included. Comparable attribute weights and level preferences were found for both BWS and DCE. Preferences were greatest for hip replacement surgery with high pain relief and the ability to participate in sports and perform daily activities. Although the estimated MARs for revision surgery followed the same trend, the MARs were systematically higher in five of the six scenarios using DCE.
CONCLUSIONS: This study confirms previous findings that BWS or DCEs are comparable in estimating attribute weights and level preferences. However, the risk tolerance threshold based on the estimation of MAR differs between these methods, possibly leading to inconsistency in comparing treatment scenarios.
Copyright © 2016 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  benefit-risk assessment; discrete choice experiment, best-worst scaling; patient preference; preference elicitation

Mesh:

Year:  2016        PMID: 27325322     DOI: 10.1016/j.jval.2015.12.020

Source DB:  PubMed          Journal:  Value Health        ISSN: 1098-3015            Impact factor:   5.725


  6 in total

Review 1.  A Systematic Review Comparing the Acceptability, Validity and Concordance of Discrete Choice Experiments and Best-Worst Scaling for Eliciting Preferences in Healthcare.

Authors:  Jennifer A Whitty; Ana Sofia Oliveira Gonçalves
Journal:  Patient       Date:  2018-06       Impact factor: 3.883

2.  Using stated-preferences methods to develop a summary metric to determine successful treatment of children with a surgical condition: a study protocol.

Authors:  Oliver Rivero-Arias; John Buckell; Benjamin Allin; Benjamin M Craig; Goher Ayman; Marian Knight
Journal:  BMJ Open       Date:  2022-06-09       Impact factor: 3.006

3.  Discrete Choice Experiments in Health Economics: Past, Present and Future.

Authors:  Vikas Soekhai; Esther W de Bekker-Grob; Alan R Ellis; Caroline M Vass
Journal:  Pharmacoeconomics       Date:  2019-02       Impact factor: 4.981

4.  What are patients' preferences for revision surgery after periprosthetic joint infection? A discrete choice experiment.

Authors:  Fran E Carroll; Rachael Gooberman-Hill; Simon Strange; Ashley W Blom; Andrew J Moore
Journal:  BMJ Open       Date:  2020-01-21       Impact factor: 2.692

5.  Patients' preferences for health insurance coverage of new technologies for treating chronic diseases in China: a discrete choice experiment.

Authors:  Jinsong Geng; Xiaowei Chen; Haini Bao; Danmin Qian; Yuting Shao; Hao Yu
Journal:  BMJ Open       Date:  2020-09-23       Impact factor: 2.692

Review 6.  Respondent Understanding in Discrete Choice Experiments: A Scoping Review.

Authors:  Alison Pearce; Mark Harrison; Verity Watson; Deborah J Street; Kirsten Howard; Nick Bansback; Stirling Bryan
Journal:  Patient       Date:  2020-11-03       Impact factor: 3.883

  6 in total

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