Discrete Choice Experiments (DCEs) investigate participants' preferences by observing their choice behavior in hypothetical scenarios and are widely used in the domain of healthcare. To reduce participants' cognitive burden, especially when dealing with a large number of attributes, researchers often employ partial profile designs. In these designs, certain attributes within each choice set are kept constant. Current literature on partial profile designs mainly focuses on main-effects models rather than interaction-effect models, with certain partial profile designs even incapable of estimating interaction effects. To address this issue, this paper introduces an Simulated Annealing (SA) algorithm to construct partial profile designs based on an interaction-effects model. During the experimental design phase, the existence and magnitude of interaction effects are often unknown. Therefore, this paper proposes a model-robust experimental design strategy. Through extensive simulation experiments and a real-life case study, we demonstrate that our SA model-robust partial profile design performs relatively well regardless of the underlying model.

翻译：离散选择实验通过观察参与者在假设情境下的选择行为来探究其偏好，在医疗领域应用广泛。为降低参与者的认知负担，特别是在处理大量属性时，研究者常采用部分剖面设计。此类设计通过固定每个选择集中的部分属性实现。当前关于部分剖面设计的文献主要关注主效应模型而非交互效应模型，某些部分剖面设计甚至无法估计交互效应。针对此问题，本文引入模拟退火算法基于交互效应模型构建部分剖面设计。由于实验设计阶段交互效应的存在性与强度通常未知，本文提出一种模型稳健的实验设计策略。通过大量仿真实验与真实案例研究，我们证明无论基础模型如何，所提出的基于模拟退火的模型稳健部分剖面设计均能保持较优性能。