{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T09:12:59Z","timestamp":1783588379029,"version":"3.55.0"},"reference-count":34,"publisher":"BMJ","issue":"1","license":[{"start":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T00:00:00Z","timestamp":1767657600000},"content-version":"unspecified","delay-in-days":5,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["bmjph"],"accepted":{"date-parts":[[2025,10,13]]},"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u2019s correlation, and response consistency was evaluated through scale heterogeneity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Preference weights from DCE and BWS-2 showed moderate concordance (Spearman\u2019s r&gt;0.70). DCE exhibited greater scale heterogeneity (\u03c4=0.931, p&lt;0.01) than BWS-2(\u03c4=0.450, p&lt;0.01), indicating more variable responses in DCE. Highly educated respondents exhibited similar levels of choice consistency (\u03c4=0.478, and 0.467 in DCE and BWS-2, p&lt;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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjph-2025-003571","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T05:15:29Z","timestamp":1767762929000},"page":"e003571","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["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"],"prefix":"10.1136","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6878-6272","authenticated-orcid":false,"given":"Shimeng","family":"Liu","sequence":"first","affiliation":[{"name":"School of Public Health, Fudan University, Shanghai, China"},{"name":"NHC Key Laboratory of Health Technology Assessment (Fudan University), Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3470-0748","authenticated-orcid":false,"given":"Yingyao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Public Health, Fudan University, Shanghai, China"},{"name":"NHC Key Laboratory of Health Technology Assessment (Fudan University), Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Public Health, Fudan University, Shanghai, China"},{"name":"NHC Key Laboratory of Health Technology Assessment (Fudan University), Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dai","family":"Lian","sequence":"additional","affiliation":[{"name":"School of Public Health, Fudan University, Shanghai, China"},{"name":"NHC Key Laboratory of Health Technology Assessment (Fudan University), Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shan","family":"Jiang","sequence":"additional","affiliation":[{"name":"Macquarie University Centre for the Health Economy, Macquarie Business School and Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8144-6220","authenticated-orcid":false,"given":"Shunping","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Social Medicine and Health Management, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan-Yuan","family":"Gu","sequence":"additional","affiliation":[{"name":"Macquarie University Centre for the Health Economy, Macquarie Business School and Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2026,1,6]]},"reference":[{"key":"2026070901150866000_4.1.e003571.1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1017\/S0266462317000241","article-title":"WHY PATIENTS SHOULD BE INVOLVED IN HEALTH TECHNOLOGY ASSESSMENT","volume":"33","author":"Wale","year":"2017","journal-title":"Int J Technol Assess Health Care"},{"key":"2026070901150866000_4.1.e003571.2","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s40271-019-00408-4","article-title":"Use of Patient Preference Studies in HTA Decision Making: A NICE Perspective","volume":"13","author":"Bouvy","year":"2020","journal-title":"Patient"},{"key":"2026070901150866000_4.1.e003571.3","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2021-068910","article-title":"Health technology assessment to inform decision making in China: progress, challenges, and sustainability","volume":"381","author":"Chen","year":"2023","journal-title":"BMJ"},{"key":"2026070901150866000_4.1.e003571.4","doi-asserted-by":"publisher","DOI":"10.1017\/S0266462310001315"},{"key":"2026070901150866000_4.1.e003571.5","doi-asserted-by":"crossref","unstructured":"Ryan M , Gerard K , AmayaAmaya M , eds. 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