The usefulness of Bayesian optimal designs for discrete choice experiments
Seminar Room 1, Newton Institute
Recently, the use of Bayesian optimal designs for discrete choice experiments has gained a lot of attention, stimulating the development of Bayesian choice design algorithms. Characteristic for the Bayesian design strategy is that it incorporates the available information about people's preferences for various product attributes in the choice design. In a first part of this talk, we show how this information can be best incorporated in the design using an experiment from health care in which preferences are measured for changes in eleven health system performance domains.
The Bayesian design methodology is in contrast with the linear design methodology which is also used in discrete choice design, and which depends for any claims of optimality on the unrealistic assumption that people have no preference for any of the attribute levels. Nevertheless, linear design principles have often been used to construct discrete choice experiments. In a second part, we show using a simulation study that the resulting utility-neutral optimal designs are not competitive with Bayesian optimal designs for estimation purposes.
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