Empirical Comparison Of Discrete Choice Experiment And Best-Worst Scaling To Estimate Stakeholders' Risk Tolerance For Hip Replacement Surgery

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.

Is Best–Worst Scaling Suitable for Health State Valuation? A Comparison with Discrete Choice Experiments

“…to make trade-offs among attributes). According to van Dijk et al (2013), the BWS tasks were perceived as being more difficult and took longer to complete than DCE tasks (van Dijk et al, 2013). This mixed evidence suggests that the 'validity and acceptability of the BWS method is not definite and requires further research'.…”

Respondent Understanding in Discrete Choice Experiments: A Scoping Review

“…Tables 1, 2 and 3 show the characteristics of the eligible studies, while Tables 4, 5 and 6 show how understanding was defined, measured, and used within each eligible study. The majority of studies were DCEs (or studies about DCEs), with two BWS3 studies [38,39] and three studies including both a DCE and a BWS3 design [40][41][42]. Most were conducted in Europe (n = 24) and the UK (n = 17), followed by the US (n = 15) and Australia (n = 10).…”

MCDA swing weighting and discrete choice experiments for elicitation of patient benefit‐risk preferences: a critical assessment

“…Previous studies in the literature have considered preference method appropriateness based on patients' preference for the elicitation task 11 and its difficulty. [8][9][10] Such studies are valuable but provide little practical guidance for a researcher who is planning a preference study. On the one hand, as this paper has shown, these factors are but a small share of the aspects the researcher should consider when choosing the preference elicitation method.…”

“…7,10 The only aspects of method appropriateness considered in these studies were which methods the patients preferred 11 or found more difficult. [8][9][10] This paper reports a qualitative comparison of 2 methods, DCE and multicriteria decision analysis (MCDA) swing weighting (SW).…”