How do Design Characteristics Affect Respondent Engagement? Assessing Attribute Non-attendance in Discrete Choice Experiments Valuing the EQ-5D-5L.

IF 3.4 3区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Patient-Patient Centered Outcomes Research Pub Date : 2025-03-15 DOI:10.1007/s40271-025-00735-9

Peiwen Jiang, Deborah Street, Richard Norman, Rosalie Viney, Mark Oppe, Brendan Mulhern

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引用次数: 0

Abstract

Introduction: Discrete choice experiments (DCEs) are increasingly applied to develop value sets for health-related quality-of-life instruments, but respondents may adopt various simplifying heuristics that affect the resulting health state values. Attribute level overlap can make these DCE tasks easier and thereby increase respondent engagement. This study uses choice tasks involving EQ-5D-5L health states to compare designs with and without overlap, constructed using different methods (generator-developed design, Ngene, SAS, and Bayesian D-efficient design) to assess respondent non-attendance to attributes.

Methods: A multi-arm DCE using the EQ-5D-5L was conducted in the Australian general population. The performance of designs with various properties was compared using the level of respondent engagement. Respondent engagement was quantified through the inferred attribute non-attendance (ANA) estimated by the equality constrained latent class model. Utility decrements derived using all respondents (i.e., including non-attendees) were compared with estimates obtained only from those who attended to all EQ-5D-5L attributes.

Results: The inclusion of overlap improved full attendance rates from 22.3-28.4% to 28.2-54.2%. Within designs with overlap, modified Fedorov designs (constructed using either Ngene or SAS macros) had higher full attendance rates than other designs. The relative attribute importance of the EQ-5D-5L also differed significantly before and after data exclusion using ANA analysis, but there was no clear pattern in the differences.

Conclusions: This study found evidence to support the use of modified Fedorov designs (constructed using Ngene or SAS) with attribute overlap to reduce ANA and improve respondent engagement in DCE studies. It highlights the potential value of ANA analysis as a quality-control tool for the inclusion and exclusion of respondents in future health valuation work for the EQ-5D-5L.

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导言：离散选择实验（DCE）越来越多地被用于为与健康相关的生活质量工具开发价值集，但受访者可能会采用各种简化启发式方法，从而影响最终的健康状态值。属性水平重叠可使这些 DCE 任务变得更容易，从而提高受访者的参与度。本研究使用涉及 EQ-5D-5L 健康状态的选择任务来比较使用不同方法（生成器开发设计、Ngene、SAS 和贝叶斯 D-效率设计）构建的有重叠和无重叠的设计，以评估受访者对属性的不重视程度：方法：在澳大利亚普通人群中使用 EQ-5D-5L 进行了一项多臂 DCE。通过受访者的参与程度来比较具有不同属性的设计的性能。受访者参与度通过平等约束潜类模型估算的推断属性不参与度（ANA）进行量化。使用所有受访者（即包括未出席者）得出的效用降低与仅从出席所有 EQ-5D-5L 属性的受访者中获得的估计值进行了比较：结果：重叠设计将全勤率从 22.3-28.4% 提高到 28.2-54.2%。在有重叠的设计中，修改后的 Fedorov 设计（使用 Ngene 或 SAS 宏构建）的全勤率高于其他设计。在使用 ANA 分析法排除数据前后，EQ-5D-5L 的相对属性重要性也有显著差异，但这种差异没有明显的模式：本研究发现，有证据支持使用属性重叠的改良费多罗夫设计（使用 Ngene 或 SAS 构建）来减少 ANA 并提高 DCE 研究中受访者的参与度。它强调了 ANA 分析作为质量控制工具的潜在价值，可用于在未来的 EQ-5D-5L 健康评估工作中纳入和排除受访者。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Patient-Patient Centered Outcomes Research HEALTH CARE SCIENCES & SERVICES-

CiteScore

6.60

自引率

8.30%

发文量

审稿时长

>12 weeks

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