比较离散选择实验与摇摆加权法估算属性相对重要性:肺癌患者偏好案例研究》。

IF 3.1 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES Medical Decision Making Pub Date : 2024-02-01 Epub Date: 2024-01-04 DOI:10.1177/0272989X231222421
J Veldwijk, I P Smith, S Oliveri, S Petrocchi, M Y Smith, L Lanzoni, R Janssens, I Huys, G A de Wit, C G M Groothuis-Oudshoorn
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引用次数: 0

摘要

简介:离散选择实验(DCE)常用于诱导患者偏好和确定属性的相对重要性,但操作复杂且成本高昂。目前有一些测量相对重要性的简单方法,如直接评级摇摆加权法(SW-DR),但很少有实证证据对两者进行比较。本研究旨在直接比较使用 DCE 和 SW-DR 得出的属性相对重要性排名和权重:方法:意大利和比利时的 307 名非小细胞肺癌患者完成了一项在线调查,使用 DCE 和 SW-DR 评估癌症治疗偏好。DCE采用随机参数Logit模型确定属性的相对重要性,SW-DR采用排序中心法(ROC)确定属性的相对重要性。使用科恩加权卡帕和 Dirichlet 回归法评估了两种方法之间相对重要性排序和权重的差异。此外,还收集了对这两项任务的理解和回答难易程度的反馈意见:大多数受访者(>65%)认为这两项任务(非常)容易理解和回答。无论采用哪种方法,生存这一属性都被评为最重要的属性。在属性的综合排名上,DCE 和 SW-ROC 之间存在显著差异(P P 结论:DCE 和 SW-ROC 之间的属性重要性存在显著差异(P P 结论:DCE 和 SW-ROC 之间的属性重要性存在显著差异(P P 结论):发现 DCE 和 SW-DR 在属性重要性方面存在显著差异。受访者表示这两种方法都比较容易理解和回答。有必要开展进一步的研究来证实这些发现。这些研究将有助于在研究各种偏好相关决策的相对属性重要性时,为方法选择提供准确的指导:大多数受访者认为 DCE 和 SW 任务非常容易或易于理解和回答。DCE 和 SW 的直接比较发现,属性重要性排序和权重存在显著差异,而且 DCE 得出的属性相对重要性权重更为分散。
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Comparing Discrete Choice Experiment with Swing Weighting to Estimate Attribute Relative Importance: A Case Study in Lung Cancer Patient Preferences.

Introduction: Discrete choice experiments (DCE) are commonly used to elicit patient preferences and to determine the relative importance of attributes but can be complex and costly to administer. Simpler methods that measure relative importance exist, such as swing weighting with direct rating (SW-DR), but there is little empirical evidence comparing the two. This study aimed to directly compare attribute relative importance rankings and weights elicited using a DCE and SW-DR.

Methods: A total of 307 patients with non-small-cell lung cancer in Italy and Belgium completed an online survey assessing preferences for cancer treatment using DCE and SW-DR. The relative importance of the attributes was determined using a random parameter logit model for the DCE and rank order centroid method (ROC) for SW-DR. Differences in relative importance ranking and weights between the methods were assessed using Cohen's weighted kappa and Dirichlet regression. Feedback on ease of understanding and answering the 2 tasks was also collected.

Results: Most respondents (>65%) found both tasks (very) easy to understand and answer. The same attribute, survival, was ranked most important irrespective of the methods applied. The overall ranking of the attributes on an aggregate level differed significantly between DCE and SW-ROC (P < 0.01). Greater differences in attribute weights between attributes were reported in DCE compared with SW-DR (P < 0.01). Agreement between the individual-level attribute ranking across methods was moderate (weighted Kappa 0.53-0.55).

Conclusion: Significant differences in attribute importance between DCE and SW-DR were found. Respondents reported both methods being relatively easy to understand and answer. Further studies confirming these findings are warranted. Such studies will help to provide accurate guidance for methods selection when studying relative attribute importance across a wide array of preference-relevant decisions.

Highlights: Both DCEs and SW tasks can be used to determine attribute relative importance rankings and weights; however, little evidence exists empirically comparing these methods in terms of outcomes or respondent usability.Most respondents found the DCE and SW tasks very easy or easy to understand and answer.A direct comparison of DCE and SW found significant differences in attribute importance rankings and weights as well as a greater spread in the DCE-derived attribute relative importance weights.

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来源期刊
Medical Decision Making
Medical Decision Making 医学-卫生保健
CiteScore
6.50
自引率
5.60%
发文量
146
审稿时长
6-12 weeks
期刊介绍: Medical Decision Making offers rigorous and systematic approaches to decision making that are designed to improve the health and clinical care of individuals and to assist with health care policy development. Using the fundamentals of decision analysis and theory, economic evaluation, and evidence based quality assessment, Medical Decision Making presents both theoretical and practical statistical and modeling techniques and methods from a variety of disciplines.
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