Centre for Health Economics Research and Evaluation, University of Technology Sydney, Sydney, Australia.
Curtin University, Perth, Australia.
Med Decis Making. 2018 Apr;38(3):306-318. doi: 10.1177/0272989X17738754. Epub 2017 Oct 31.
Discrete Choice Experiments including duration (DCE) can be used to generate utility values for health states from measures such as EQ-5D-5L. However, methodological issues concerning the optimum way to present choice sets remain. The aim of the present study was to test a range of task presentation approaches designed to support the DCE completion process.
Four separate presentation approaches were developed to examine different task features including dimension level highlighting, and health state severity and duration level presentation. Choice sets included 2 EQ-5D-5L states paired with 1 of 4 duration levels, and a third "immediate death" option. The same design, including 120 choice sets (developed using optimal methods), was employed across all approaches. The online survey was administered to a sample of the Australian population who completed 20 choice sets across 2 approaches. Conditional logit regression was used to assess model consistency, and scale parameter testing investigated poolability.
Overall 1,565 respondents completed the survey. Three approaches, using different dimension level highlighting techniques, produced mainly monotonic coefficients that resulted in a larger disutility as the severity level increased (excepting usual activities levels 2/3). The fourth approach, using a level indicator to present the severity levels, has slightly more non-monotonicity and produced larger ordered differences for the more severe dimension levels. Scale parameter testing suggested that the data cannot be pooled.
The results provide information regarding how to present DCE tasks for health state valuation. The findings improve our understanding of the impact of different presentation approaches on valuation, and how DCE questions could be presented to be amenable to completion. However, it is unclear if the task presentation impacts online respondent engagement.
离散选择实验(DCE)可用于从 EQ-5D-5L 等措施中生成健康状态的效用值。然而,关于呈现选择集的最佳方式仍存在方法学问题。本研究旨在测试一系列旨在支持 DCE 完成过程的任务呈现方法。
开发了四种不同的呈现方法,以检查不同的任务特征,包括维度水平突出显示以及健康状态严重程度和持续时间水平呈现。选择集包括 2 个 EQ-5D-5L 状态,每个状态与 4 个持续时间水平中的 1 个配对,以及第三个“立即死亡”选项。所有方法均采用相同的设计,包括 120 个选择集(使用最佳方法开发)。在线调查在澳大利亚人群中进行,该人群完成了 2 种方法中的 20 个选择集。条件逻辑回归用于评估模型一致性,而规模参数测试则研究了可组合性。
共有 1565 名受访者完成了调查。三种方法使用不同的维度水平突出显示技术,主要产生单调系数,随着严重程度的增加导致更大的不效用(除了通常活动水平 2/3)。第四种方法使用水平指示器呈现严重程度,具有略微更多的非单调性,并产生了更严重维度水平的更大有序差异。规模参数测试表明数据不可组合。
研究结果提供了有关如何呈现健康状态估值的 DCE 任务的信息。研究结果提高了我们对不同呈现方法对估值的影响的理解,以及如何呈现 DCE 问题以使其易于完成。然而,尚不清楚任务呈现是否会影响在线受访者的参与度。