Designing choice experiments with many attributes. An application to setting priorities for orthopaedic waiting lists
采用分块属性设计进行离散选择实验，将11个属性分配到三个子设计并合并数据，以解决多属性选择实验中属性数量受限导致的遗漏变量偏差问题，适用于需要为多属性质量评价工具赋权的情形。
The aim of this paper is to undertake a discrete choice experiment using a 'blocked attribute' design. To date in the health economics literature, most discrete choice experiments have used only a relatively small number of attributes due to concerns about task complexity, non-compensatory decision rules, simplicity of experimental designs, and the costs of surveys. This may lead to omitted variable bias and reduced explanatory power when attributes have been pre-selected from a longer list. There may be situations where it is desirable to include a longer list of attributes, such as attaching weights to quality-of-life instruments to obtain single index scores. We examine this issue in the context of attaching weights to a disease-specific quality-of-life instrument used to prioritise patients on orthopaedic waiting lists in Victorian hospitals. Eleven attributes are allocated across three separate experimental designs and the data pooled for analysis. Pooling is justified given the specific context of the study, including attempts to minimise the effect of unobserved heterogeneity across the three models when designing the study and collecting data. Blocked attribute designs may offer flexibility to researchers when it is not possible or desirable to reduce the number of attributes.