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

Objective Incorporating patient perspectives is essential in health technology assessments. Selecting appropriate tools, such as discrete choice experiment (DCE) or best-worst scaling (BWS), is critical for generating high-quality evidence. This study aims to compare preference estimates and response consistency between DCE and BWS profile case (BWS-2) and provide empirical guidance on the optimal method for patient preference elicitation. Methods Multistage stratified cluster sampling was used to recruit 3327 patients with type 2 diabetes mellitus (T2DM) from 50 health institutions for face-to-face interviews conducted from September to December 2022. Participants completed DCE and BWS-2 tasks involving hypothetical antihyperglycaemic medication profiles defined by seven attributes: efficacy, hypoglycaemia risk, cardiovascular benefits, gastrointestinal adverse events, weight change, mode of administration and out-of-pocket cost. Each participant completed six DCE and six BWS-2 tasks in randomised order (DCE first followed by BWS, or BWS first followed by DCE). Responses were analysed using mixed logit and generalised multinomial logit models. Method concordance was assessed with Spearman’s correlation, and response consistency was evaluated through scale heterogeneity. Results Preference weights from DCE and BWS-2 showed moderate concordance (Spearman’s r>0.70). DCE exhibited greater scale heterogeneity (τ=0.931, p<0.01) than BWS-2(τ=0.450, p<0.01), indicating more variable responses in DCE. Highly educated respondents exhibited similar levels of choice consistency (τ=0.478, and 0.467 in DCE and BWS-2, p<0.01, respectively) regardless of the preference elicitation tool used. However, respondents with lower education levels or illiteracy displayed higher choice consistency in BWS-2. Conclusion While DCE and BWS-2 produced comparable preference estimates, the higher consistency in BWS-2 responses suggests it may be more reliable for eliciting patient medication preferences, especially those lower educated or illiteracy patients.

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Publication Details

Journal
BMJ Public Health
Published
2026-01-01
DOI
https://doi.org/10.1136/bmjph-2025-003571
Citations
1
Primary Topic
Economic and Environmental Valuation
Type
article
Field-Weighted Citation Impact
99.81

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article

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

Shan Jiang, Yuanyuan Gu, YingYao Chen, Shimeng Liu et al.
1 citations
BMJ Public Health
Economic and Environmental Valuation
99.81
article

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

Shan Jiang, Yuanyuan Gu, YingYao Chen, Shimeng Liu, Ying Tao, Shunping Li, Dai Lian
article en
1 citations

Abstract

Objective Incorporating patient perspectives is essential in health technology assessments. Selecting appropriate tools, such as discrete choice experiment (DCE) or best-worst scaling (BWS), is critical for generating high-quality evidence. This study aims to compare preference estimates and response consistency between DCE and BWS profile case (BWS-2) and provide empirical guidance on the optimal method for patient preference elicitation. Methods Multistage stratified cluster sampling was used to recruit 3327 patients with type 2 diabetes mellitus (T2DM) from 50 health institutions for face-to-face interviews conducted from September to December 2022. Participants completed DCE and BWS-2 tasks involving hypothetical antihyperglycaemic medication profiles defined by seven attributes: efficacy, hypoglycaemia risk, cardiovascular benefits, gastrointestinal adverse events, weight change, mode of administration and out-of-pocket cost. Each participant completed six DCE and six BWS-2 tasks in randomised order (DCE first followed by BWS, or BWS first followed by DCE). Responses were analysed using mixed logit and generalised multinomial logit models. Method concordance was assessed with Spearman’s correlation, and response consistency was evaluated through scale heterogeneity. Results Preference weights from DCE and BWS-2 showed moderate concordance (Spearman’s r>0.70). DCE exhibited greater scale heterogeneity (τ=0.931, p<0.01) than BWS-2(τ=0.450, p<0.01), indicating more variable responses in DCE. Highly educated respondents exhibited similar levels of choice consistency (τ=0.478, and 0.467 in DCE and BWS-2, p<0.01, respectively) regardless of the preference elicitation tool used. However, respondents with lower education levels or illiteracy displayed higher choice consistency in BWS-2. Conclusion While DCE and BWS-2 produced comparable preference estimates, the higher consistency in BWS-2 responses suggests it may be more reliable for eliciting patient medication preferences, especially those lower educated or illiteracy patients.

BMJ Public HealthVol. 4(1)
Shandong University (CN), Fudan University (CN), Macquarie University (AU)
National Natural Science Foundation of China
Openalex Percentile: Top 1%
Economic and Environmental Valuation
99.81
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