Centre for Health Services and Policy Research, University of British Columbia, 201-2206 East Mall, Vancouver, British Columbia V6T 1Z3, Canada.
J Clin Epidemiol. 2012 Aug;65(8):887-96. doi: 10.1016/j.jclinepi.2012.02.010. Epub 2012 Apr 9.
The objective was to determine whether a paired-comparison/Leaning Scale (LS) method: 1) could feasibly be used to elicit strength-of-preference scores for elective health care options in large community-based survey settings and 2) could reveal preferential subgroups that would have been overlooked if only a categorical-response format had been used.
Medicare beneficiaries in four different regions of the United States were interviewed in person. Participants considered eight clinical scenarios, each with two to three different health care options. For each scenario, participants categorically selected their favored option, then indicated how strongly they favored that option relative to the alternative on a paired-comparison bidirectional LS.
Two hundred two participants were interviewed. For seven of the eight scenarios, a clear majority (>50%) indicated that, overall, they categorically favored one option over the alternative(s). However, the bidirectional strength-of-preference LS scores revealed that, in four scenarios, for half of those participants, their preference for the favored option was actually "weak" or "neutral."
Investigators aiming to assess population-wide preferential attitudes toward different elective health care scenarios should consider gathering ordinal-level strength-of-preference scores and could feasibly use the paired-comparison/bidirectional LS to do so.
旨在确定配对比较/学习量表(LS)方法:1)是否可以在大型基于社区的调查环境中,合理地用于获取对可选医疗保健方案的偏好强度评分;2)是否可以揭示如果仅使用分类响应格式,可能会忽略的优先亚组。
在美国四个不同地区的 Medicare 受益人接受了个人访谈。参与者考虑了八种临床情况,每种情况都有两种或三种不同的医疗保健选择。对于每种情况,参与者都在分类上选择了他们喜欢的方案,然后在配对比较双向 LS 上表示相对于另一种方案他们对该方案的偏好强度。
对 202 名参与者进行了访谈。在八种情况中的七种情况下,绝大多数(>50%)表示,总体而言,他们在分类上优先选择一种方案而不是另一种方案。然而,双向偏好强度 LS 评分显示,在四个方案中,对于一半的参与者来说,他们对首选方案的偏好实际上是“弱”或“中性”。
旨在评估人群对不同可选医疗保健方案的偏好态度的研究人员应考虑收集有序水平的偏好强度评分,并且可以合理地使用配对比较/双向 LS 来进行评估。