Abstract
Given the increasing number of genetic tests available, decisions have to be made on how to allocate limited health-care resources to them. Different criteria have been proposed to guide priority setting. However, their relative importance is unclear. Discrete-choice experiments (DCEs) and best-worst scaling experiments (BWSs) are methods used to identify and weight various criteria that influence orders of priority. This study tests whether these preference eliciting techniques can be used for prioritising genetic tests and compares the empirical findings resulting from these two approaches. Pilot DCE and BWS questionnaires were developed for the same criteria: prevalence, severity, clinical utility, alternatives to genetic testing available, infrastructure for testing and care established, and urgency of care. Interview-style experiments were carried out among different genetics professionals (mainly clinical geneticists, researchers and biologists). A total of 31 respondents completed the DCE and 26 completed the BWS experiment. Weights for the levels of the six attributes were estimated by conditional logit models. Although the results derived from the DCE and BWS experiments differed in detail, we found similar valuation patterns in the DCE and BWS experiments. The respondents attached greatest value to tests with high clinical utility (defined by the availability of treatments that reduce mortality and morbidity) and to testing for highly prevalent conditions. The findings from this study exemplify how decision makers can use quantitative preference eliciting methods to measure aggregated preferences in order to prioritise alternative clinical interventions. Further research is necessary to confirm the survey results.