Designing Choice Experiments with Many Attributes: An Application to Setting Priorities for Orthopaedic Waiting Lists
Stated preference discrete choice experiments are being increasingly used to value the quality of health care services. To date in the health economics literature, 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. The aim of this paper is to examine the feasibility of using a ‘blocked attribute’ design in a DCE with 11 attributes. This method allocates the 11 attributes across three separate experimental designs and pools the data for analysis. We examine this issue in the context of attaching weights to a disease specific quality of life instrument used to prioritise orthopaedic waiting lists in Victorian hospitals. We produce a single index measure of utility for health states of patients, bounded between zero and one. The use of such a design seems feasible, although issues remain to be resolved about how the ranking should be used in practice to set priorities for waiting lists.
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