Department of Health Technology and Services Research, University of Twente, Enschede, the Netherlands.
Patient. 2012;5(1):45-56. doi: 10.2165/11587140-000000000-00000.
With growing emphasis on patient involvement in health technology assessment, there is a need for scientific methods that formally elicit patient preferences. Analytic hierarchy process (AHP) and conjoint analysis (CA) are two established scientific methods - albeit with very different objectives.
The objective of this study was to compare the performance of AHP and CA in eliciting patient preferences for treatment alternatives for stroke rehabilitation.
Five competing treatments for drop-foot impairment in stroke were identified. One survey, including the AHP and CA questions, was sent to 142 patients, resulting in 89 patients for final analysis (response rate 63%). Standard software was used to calculate attribute weights from both AHP and CA. Performance weights for the treatments were obtained from an expert panel using AHP. Subsequently, the mean predicted preference for each of the five treatments was calculated using the AHP and CA weights. Differences were tested using non-parametric tests. Furthermore, all treatments were rank ordered for each individual patient, using the AHP and CA weights.
Important attributes in both AHP and CA were the clinical outcome (0.3 in AHP and 0.33 in CA) and risk of complications (about 0.2 in both AHP and CA). Main differences between the methods were found for the attributes 'impact of treatment' (0.06 for AHP and 0.28 for two combined attributes in CA) and 'cosmetics and comfort' (0.28 for two combined attributes in AHP and 0.05 for CA). On a group level, the most preferred treatments were soft tissue surgery (STS) and orthopedic shoes (OS). However, STS was most preferred using AHP weights versus OS using CA weights (p < 0.001). This difference was even more obvious when interpreting the individual treatment ranks. Nearly all patients preferred STS according to the AHP predictions, while >50% of the patients chose OS instead of STS, as most preferred treatment using CA weights.
While we found differences between AHP and CA, these differences were most likely caused by the labeling of the attributes and the elicitation of performance judgments. CA scenarios are built using the level descriptions, and hence provide realistic treatment scenarios. In AHP, patients only compared less concrete attributes such as 'impact of treatment.' This led to less realistic choices, and thus overestimation of the preference for the surgical scenarios. Several recommendations are given on how to use AHP and CA in assessing patient preferences.
随着越来越重视患者在卫生技术评估中的参与，需要有正式征求患者偏好的科学方法。层次分析法（AHP）和联合分析（CA）是两种已确立的科学方法-尽管目标非常不同。
本研究旨在比较 AHP 和 CA 在诱发中风康复治疗替代方案患者偏好方面的性能。
确定了 5 种用于治疗中风足下垂的竞争治疗方法。一项调查包括 AHP 和 CA 问题，共发送给 142 名患者，最终有 89 名患者进行了最终分析（应答率为 63％）。使用标准软件从 AHP 和 CA 中计算出属性权重。使用 AHP 从专家小组获得治疗方法的绩效权重。随后，使用 AHP 和 CA 权重计算出五种治疗方法的平均预测偏好。使用非参数检验检验差异。此外，使用 AHP 和 CA 权重，对每个个体患者对所有治疗方法进行了排序。
AHP 和 CA 中的重要属性均为临床结果（AHP 中为 0.3，CA 中为 0.33）和并发症风险（AHP 和 CA 中均约为 0.2）。两种方法之间的主要区别在于“治疗效果”（AHP 中为 0.06，CA 中为两个综合属性的 0.28）和“美容和舒适”（AHP 中为两个综合属性的 0.28，CA 中为 0.05）。在组水平上，最受欢迎的治疗方法是软组织手术（STS）和矫形鞋（OS）。但是，根据 AHP 权重，STS 是最受欢迎的治疗方法，而根据 CA 权重，OS 是最受欢迎的治疗方法（p <0.001）。当解释个别治疗排名时，这种差异更为明显。几乎所有患者根据 AHP 预测都更喜欢 STS，而> 50％的患者选择 OS 而不是 STS，因为这是 CA 权重下最受欢迎的治疗方法。
尽管我们发现 AHP 和 CA 之间存在差异，但这些差异很可能是由于属性的标记和绩效判断的启发引起的。CA 场景是使用级别描述构建的，因此提供了现实的治疗场景。在 AHP 中，患者仅比较不太具体的属性，例如“治疗效果”。这导致了不太现实的选择，从而高估了对手术场景的偏好。就如何使用 AHP 和 CA 评估患者偏好提出了一些建议。
