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This chapter walks the reader through the stages of a discrete choice experiment (DCE) application drawing on concepts introduced in Chapters 1 and 2. A case study eliciting women’s preferences for prenatal screening is used to illustrate the points (Ryan et al., 2005). It should be noted that this data was collected several years ago, and therefore adopted old methods of experimental design. The sample size is also small.We have chosen it because it demonstrates nicely the many potential uses of a DCE, it addressed a policy-relevant question at the time, and it represents one of the few studies in health economics where the scientists (geneticists) worked with the evaluators (economists) and implementers (obstetricians) to look at development, evaluation and implementation of prenatal screening programmes.

[...] [T]he application of discrete choice experiments (DCEs) in health economics has seen an increase over the last few years. While the number of studies using DCEs is growing, there has been relatively limited consideration of experimental design theory and methods. Details of the development of the designed experiment are rarely discussed. Many studies have used small fractional factorial designs (FFDs), generated with commercial design software packages, e.g. orthogonal main effects plans (OMEPs), sometimes manipulated in ad hoc ways (e.g. randomly pairing up scenarios or taking one scenario from the design and combining it with every other scenario). Such approaches can result in designs with unknown statistical design properties, in particular with unknown correlations between parameter estimates.