Wickramasekera Nyantara, Shackley Phil, Rowen Donna
Sheffield Centre for Health and Related Research (SCHARR), The University of Sheffield, Sheffield, United Kingdom.
J Med Internet Res. 2025 Mar 21;27:e59209. doi: 10.2196/59209.
Decision aids empower patients to understand how treatment options match their preferences. Choice experiments, a method to clarify values used within decision aids, present patients with hypothetical scenarios to reveal their preferences for treatment characteristics. Given the rise in research embedding choice experiments in decision tools and the emergence of novel developments in embedding methodology, a scoping review is warranted.
This scoping review examines how choice experiments are embedded into decision tools and how these tools are evaluated, to identify best practices.
This scoping review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Searches were conducted on MEDLINE, PsycInfo, and Web of Science. The methodology, development and evaluation details of decision aids were extracted and summarized using narrative synthesis.
Overall, 33 papers reporting 22 tools were included in the scoping review. These tools were developed for various health conditions, including musculoskeletal (7/22, 32%), oncological (8/22, 36%), and chronic conditions (7/22, 32%). Most decision tools (17/22, 77%) were developed in the United States, with the remaining tools originating in the Netherlands, United Kingdom, Canada, and Australia. The number of publications increased, with 73% (16/22) published since 2015, peaking at 4 publications in 2019. The primary purpose of these tools (20/22, 91%) was to help patients compare or choose treatments. Adaptive conjoint analysis was the most frequently used design type (10/22, 45%), followed by conjoint analysis and discrete choice experiments (DCEs; both 4/22, 18%), modified adaptive conjoint analysis (3/22, 14%), and adaptive best-worst conjoint analysis (1/22, 5%). The number of tasks varied depending on the design (6-12 for DCEs and adaptive conjoint vs 16-20 for conjoint analysis designs). Sawtooth software was commonly used (14/22, 64%) to embed choice tasks. Four proof-of-concept embedding methods were identified: scenario analysis, known preference phenotypes, Bayesian collaborative filtering, and penalized multinomial logit model. After completing the choice tasks patients received tailored information, 73% (16/22) of tools provided attribute importance scores, and 23% (5/22) presented a "best match" treatment ranking. To convey probabilistic attributes, most tools (13/22, 59%) used a combination of approaches, including percentages, natural frequencies, icon arrays, narratives, and videos. The tools were evaluated across diverse study designs (randomized controlled trials, mixed methods, and cohort studies), with sample sizes ranging from 23 to 743 participants. Over 40 different outcomes were included in the evaluations, with the decisional conflict scale being the most frequently used in 6 tools.
This scoping review provides an overview of how choice experiments are embedded into decision tools. It highlights the lack of established best practices for embedding methods, with only 4 proof-of-concept methods identified. Furthermore, the review reveals a lack of consensus on outcome measures, emphasizing the need for standardized outcome selection for future evaluations.
决策辅助工具可帮助患者了解治疗方案如何符合其偏好。选择实验是一种用于阐明决策辅助工具中所使用价值的方法，它向患者呈现假设情景以揭示他们对治疗特征的偏好。鉴于将选择实验嵌入决策工具的研究不断增加以及嵌入方法出现了新进展，有必要进行一项范围综述。
本范围综述考察选择实验如何嵌入决策工具以及这些工具如何被评估，以确定最佳实践。
本范围综述遵循PRISMA（系统评价与Meta分析扩展版范围综述的首选报告项目）指南。在MEDLINE、PsycInfo和科学网进行检索。使用叙述性综合方法提取并总结决策辅助工具的方法、开发和评估细节。
总体而言，范围综述纳入了33篇报告22种工具的论文。这些工具针对各种健康状况开发，包括肌肉骨骼疾病（7/22，32%）、肿瘤疾病（8/22，36%）和慢性病（7/22，32%）。大多数决策工具（17/22，77%）在美国开发，其余工具来自荷兰、英国、加拿大和澳大利亚。出版物数量有所增加，自2015年以来有73%（16/22）发表，在2019年达到4篇的峰值。这些工具的主要目的（20/22，91%）是帮助患者比较或选择治疗方法。适应性联合分析是最常用的设计类型（10/22，45%），其次是联合分析和离散选择实验（DCEs；均为4/22，18%）、改良适应性联合分析（3/22，14%）以及适应性最佳-最差联合分析（1/22，5%）。任务数量因设计而异（DCEs和适应性联合分析为6 - 12个，联合分析设计为16 - 20个）。Sawtooth软件常用于（14/22，64%）嵌入选择任务。确定了四种概念验证嵌入方法：情景分析、已知偏好表型、贝叶斯协同过滤和惩罚多项logit模型。完成选择任务后，患者会收到量身定制的信息，73%（16/22）的工具提供属性重要性得分，23%（5/22）呈现“最佳匹配”治疗排名。为传达概率属性，大多数工具（13/22，59%）使用多种方法的组合，包括百分比、自然频率、图标阵列、叙述和视频。这些工具通过多种研究设计（随机对照试验、混合方法和队列研究）进行评估，样本量从23至743名参与者不等。评估中纳入了40多种不同的结果，决策冲突量表在6种工具中使用最为频繁。
本范围综述概述了选择实验如何嵌入决策工具。它突出了嵌入方法缺乏既定最佳实践的情况，仅确定了4种概念验证方法。此外，综述揭示了在结果测量方面缺乏共识，强调未来评估需要标准化的结果选择。
