Preference Measurement for Multistate Health Profiles

描述（由申请人提供）：本研究旨在开发和测试一种新方法，该方法可以更好地捕获对多州健康概况的偏好。其动机是由于QALY（质量调整生命年）模型未能捕获对多状态健康概况的偏好。正如过去的文献所示，传统的QALY模型违反了它的一个必要假设，即加性独立性。这些发现表明，个体健康状态之间的偏好并不是相互独立的。本研究提出了一种新颖的方法，旨在通过一次观察两个连续的健康状态来衡量对多州健康概况的偏好。它假设对未来健康状态的评估依赖于或“以”当前健康状态为条件。被怀疑影响未来健康状态“条件偏好得分”的当前健康状态特征包括当前健康状态的持续时间、变化的方向以及当前和未来健康状态之间变化的幅度。将采用全因子设计（三个因素，每个因素两个水平），在未来健康状态的三个不同水平上进行三个重复，以探索主要影响和相互作用。此外，本研究将检验所提出的评估多状态健康状况的“条件偏好得分”的技术是否比无条件健康状况评估能更好地预测整个健康状况的偏好得分。持续时间加权条件偏好得分和持续时间加权无条件偏好得分将与直接引出的10个假设健康概况的整体得分进行比较，每个健康概况由四种健康状态的精心选择组合组成。两种启发技术将被用于所有的任务，视觉模拟量表（VAS）和时间权衡（TTO）。功率分析显示，每种技术的样本量为70个受试者，将有至少80%的概率检测到小至0.05的效应值。受试者将从佐治亚理工学院的学生群体中招募。人类受试者已获得批准。

DESCRIPTION (PROVIDED BY APPLICANT): This study aims at developing and testing a new method that can better capture preference for multistate health profiles. The motivation arose from the failure of the QALY (quality-adjusted life-year) model in capturing preferences for multistate health profiles. As past literature shows, the conventional QALY model violates one of its required assumptions, the additive independence. These findings imply that preferences between individual health states are not independent of each other. This study proposes a novel approach designed to measure preferences for multistate health profiles by looking at two consecutive health states at a time. It hypothesizes that the evaluation of a future health state is dependent or "conditioned" on the current health state. Characteristics of the current health state which are suspected to affect the "conditional preference scores" for future health state include duration of the current health state, direction of change, and the amplitude of change between the current and future health states. A full factorial design (three factors with two levels each) with three replications at three different levels of the future health state will be used to explore both main effects and interactions. Furthermore, this study will test whether the proposed technique, which assesses "conditional preference scores" for multistate health profiles, can better predict preference scores for an entire health profile than the unconditional health state assessments. Duration-weighted conditional preference scores and duration-weighted unconditional preference scores will be compared to directly elicited holistic scores for 10 hypothetical health profiles, each composed of carefully selected combinations of four health states. Two elicitation techniques will be employed for all tasks, visual analog scale (VAS) and time-tradeoff (TTO). A power analysis revealed that a sample size of 70 subjects for each technique will give at least 80% probability of detecting an effect size as small as 0.05. Subjects will be recruited from the student population at Georgia Tech. Human subjects approval has been obtained.