Department of Economics, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL, CMC206A33620, USA.
National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Patient. 2024 Nov;17(6):635-644. doi: 10.1007/s40271-024-00708-4. Epub 2024 Jul 20.
Kaizen is a Japanese term for continuous improvement (kai ~ change, zen ~ good). In a kaizen task, a respondent makes sequential choices to improve an object's profile, revealing a preference path. Including kaizen tasks in a discrete choice experiment has the advantage of collecting greater preference evidence than pick-one tasks, such as paired comparisons. OBJECTIVE AND METHODS: So far, three online discrete choice experiments have included kaizen tasks: the 2020 US COVID-19 vaccination (CVP) study, the 2021 UK Children's Surgery Outcome Reporting (CSOR) study, and the 2023 US EQ-5D-Y-3L valuation (Y-3L) study. In this evidence synthesis, we describe the performance of the kaizen tasks in terms of response behaviors, conditional logit and Zermelo-Bradley-Terry (ZBT) estimates, and their standard errors in each of the surveys.
Comparing the CVP and Y-3L, including hold-outs (i.e., attributes shared by all alternatives) seems to reduce positional behavior by half. The CVP tasks excluded multi-level improvements; therefore, we could not estimate logit main effects directly. In the CSOR, only 12 of the 21 logit estimates are significantly positive (p < 0.05), possibly due to the fixed attribute order. All Y-3L estimates are significantly positive, and their predictions are highly correlated (Pearson: logit 0.802, ZBT 0.882) and strongly agree (Lin: logit 0.744, ZBT 0.852) with the paired-comparison probabilities.
These discrete choice experiments offer important lessons for future studies: (1) include warm-up tasks, hold-outs, and multi-level improvements; (2) randomize the attribute order (i.e., up-down) at the respondent level; and (3) recruit smaller samples of respondents than traditional discrete choice experiments with only pick-one tasks.
Kaizen 是日语中持续改进的意思（kai改变，zen好）。在 Kaizen 任务中，受访者会做出一系列选择来改善对象的特征，揭示偏好路径。在离散选择实验中包含 Kaizen 任务具有比单项选择任务（如配对比较）收集更多偏好证据的优势。
迄今为止，已有三项在线离散选择实验包含 Kaizen 任务：2020 年美国 COVID-19 疫苗接种（CVP）研究、2021 年英国儿童手术结果报告（CSOR）研究和 2023 年美国 EQ-5D-Y-3L 估值（Y-3L）研究。在这项证据综合研究中，我们根据受访者的反应行为、条件逻辑和 Zermelo-Bradley-Terry（ZBT）估计值，以及每个调查中的标准误差，描述 Kaizen 任务的性能。
将 CVP 和 Y-3L 进行比较，包括保留项（即所有备选方案共有的属性）似乎将位置行为减半。CVP 任务排除了多层次的改进；因此，我们无法直接估计对数主效应。在 CSOR 中，只有 21 个对数估计中有 12 个显著为正（p<0.05），可能是由于属性顺序固定所致。所有 Y-3L 估计均显著为正，且其预测高度相关（Pearson：logit 0.802，ZBT 0.882），与配对比较概率高度一致（Lin：logit 0.744，ZBT 0.852）。
这些离散选择实验为未来的研究提供了重要的经验教训：（1）包含预热任务、保留项和多层次改进；（2）在受访者层面随机化属性顺序（即上下）；（3）与仅包含单项选择任务的传统离散选择实验相比，招募的受访者样本量较小。
