Danner Marion, Vennedey Vera, Hiligsmann Mickaël, Fauser Sascha, Gross Christian, Stock Stephanie
Institute for Health Economics and Clinical Epidemiology, University Hospital of Cologne (AöR), Cologne, Germany.
Institute for Health Economics and Clinical Epidemiology, University Hospital of Cologne (AöR), Cologne, Germany.
Value Health. 2017 Sep;20(8):1166-1173. doi: 10.1016/j.jval.2017.04.022. Epub 2017 May 31.
In this study, we conducted an analytic hierarchy process (AHP) and a discrete choice experiment (DCE) to elicit the preferences of patients with age-related macular degeneration using identical attributes and levels.
To compare preference-based weights for age-related macular degeneration treatment attributes and levels generated by two elicitation methods. The properties of both methods were assessed, including ease of instrument use.
A DCE and an AHP experiment were designed on the basis of five attributes. Preference-based weights were generated using the matrix multiplication method for attributes and levels in AHP and a mixed multinomial logit model for levels in the DCE. Attribute importance was further compared using coefficient (DCE) and weight (AHP) level ranges. The questionnaire difficulty was rated on a qualitative scale. Patients were asked to think aloud while providing their judgments.
AHP and DCE generated similar results regarding levels, stressing a preference for visual improvement, frequent monitoring, on-demand and less frequent injection schemes, approved drugs, and mild side effects. Attribute weights derived on the basis of level ranges led to a ranking that was opposite to the AHP directly calculated attribute weights. For example, visual function ranked first in the AHP and last on the basis of level ranges.
The results across the methods were similar, with one exception: the directly measured AHP attribute weights were different from the level-based interpretation of attribute importance in both DCE and AHP. The dependence/independence of attribute importance on level ranges in DCE and AHP, respectively, should be taken into account when choosing a method to support decision making.
在本研究中,我们进行了层次分析法(AHP)和离散选择实验(DCE),以使用相同的属性和水平来引出年龄相关性黄斑变性患者的偏好。
比较两种引出方法生成的年龄相关性黄斑变性治疗属性和水平的基于偏好的权重。评估了这两种方法的特性,包括仪器使用的简便性。
基于五个属性设计了一个DCE和一个AHP实验。使用AHP中属性和水平的矩阵乘法方法以及DCE中水平的混合多项logit模型生成基于偏好的权重。使用系数(DCE)和权重(AHP)水平范围进一步比较属性重要性。问卷难度采用定性量表进行评分。要求患者在做出判断时大声思考。
AHP和DCE在水平方面产生了相似的结果,强调了对视力改善、频繁监测、按需和较少频繁注射方案、获批药物以及轻微副作用的偏好。基于水平范围得出的属性权重导致的排名与直接计算的AHP属性权重相反。例如,视觉功能在AHP中排名第一,而基于水平范围则排名最后。
各方法的结果相似,但有一个例外:直接测量的AHP属性权重与DCE和AHP中基于水平的属性重要性解释不同。在选择支持决策的方法时,应分别考虑DCE和AHP中属性重要性对水平范围的依赖性/独立性。