Department of Public Health, Erasmus MC, University Medical Centre Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands,
Pharmacoeconomics. 2013 Dec;31(12):1169-83. doi: 10.1007/s40273-013-0100-3.
Most researchers in health economics cite random utility theory (RUT) when analysing discrete choice experiments (DCEs). Under RUT, the error term is associated with the analyst's inability to properly capture the true choice processes of the respondent as well as the inconsistency or mistakes arising from the respondent themselves. Under such assumptions, it stands to reason that analysts should explore more complex nonlinear indirect utility functions, than currently used in healthcare, to strive for better estimates of preferences in healthcare.
To test whether complex indirect utility functions decrease error variance for models that either implicitly (i.e. the multinomial logit (MNL) model) or explicitly (i.e. entropy multinomial logit (EMNL) model) account for error variance in health(care)-related DCEs; and to determine the impact of complex indirect utility functions on willingness-to-pay (WTP) measures.
Using data from DCEs aimed at healthcare-related decisions, we empirically compared (1) complex and simple indirect utility specifications in terms of goodness of fit, (2) their impact on WTP measures, including confidence intervals (CIs) based on the Delta method, the Krinsky and Robb-procedure, and Bootstrapping, and (3) MNL and EMNL model results.
Complex indirect utility functions had a better model fit than simple specifications (p < 0.05). WTP estimates were quite similar across alternative specifications. The Delta method produced the most narrow CIs. The EMNL model showed that respondents apply simplifying strategies when answering DCE questions.
Complex indirect utility functions reduce error arisen from researchers, which can have important implications for measures in healthcare such as the WTP, whereas EMNL provides insights into the behaviour of respondents when answering DCEs. Understanding how respondents answer DCE questions may allow researchers to construct DCEs that minimise scale differences, so that the decision error made across respondents is more homogeneous and therefore taken out as additional noise in the data. Hence, better estimates of preferences in healthcare can be provided.
大多数健康经济学研究人员在分析离散选择实验（DCE）时都会引用随机效用理论（RUT）。根据 RUT，误差项与分析师无法正确捕捉受访者真实选择过程以及受访者自身的不一致或错误有关。在这种假设下，分析师应该探索比当前在医疗保健中使用的更复杂的非线性间接效用函数，以努力更好地估计医疗保健中的偏好。
测试复杂的间接效用函数是否会降低隐含（即多项逻辑回归（MNL）模型）或明确（即熵多项逻辑回归（EMNL）模型）考虑与健康相关的 DCE 中误差方差的模型的误差方差；并确定复杂间接效用函数对支付意愿（WTP）度量的影响。
使用来自旨在针对医疗保健相关决策的 DCE 的数据，我们从以下几个方面进行了实证比较：（1）复杂和简单间接效用规范在拟合优度方面的比较；（2）它们对 WTP 度量的影响，包括基于 Delta 方法、Krinke 和 Robb 程序以及Bootstrapping 的置信区间（CI）；（3）MNL 和 EMNL 模型结果。
复杂的间接效用函数比简单的规范具有更好的模型拟合度（p<0.05）。替代规范下的 WTP 估计值非常相似。Delta 方法产生的 CI 最窄。EMNL 模型表明，受访者在回答 DCE 问题时采用了简化策略。
复杂的间接效用函数减少了研究人员产生的误差，这对医疗保健中的支付意愿等措施具有重要意义，而 EMNL 则提供了受访者在回答 DCE 时的行为洞察力。了解受访者如何回答 DCE 问题可以使研究人员构建可以最小化规模差异的 DCE，从而使跨受访者的决策误差更加同质，并因此在数据中作为额外的噪声被剔除。因此，可以提供更好的医疗保健偏好估计。
