ESTIMATING HEALTH STATE UTILITY VALUES FROM DISCRETE CHOICE EXPERIMENTS—A QALY SPACE MODEL APPROACH
提出基于混合logit的QALY空间模型来改进离散选择实验估计健康效用值的方法，发现该模型比条件logit给出更低的效用估计，且差异随健康状态恶化而增大。
Using discrete choice experiments (DCEs) to estimate health state utility values has become an important alternative to the conventional methods of Time Trade-Off and Standard Gamble. Studies using DCEs have typically used the conditional logit to estimate the underlying utility function. The conditional logit is known for several limitations. In this paper, we propose two types of models based on the mixed logit: one using preference space and the other using quality-adjusted life year (QALY) space, a concept adapted from the willingness-to-pay literature. These methods are applied to a dataset collected using the EQ-5D. The results showcase the advantages of using QALY space and demonstrate that the preferred QALY space model provides lower estimates of the utility values than the conditional logit, with the divergence increasing with worsening health states.