Preference heterogeneity is commonly encountered in discrete choice experiments (DCEs) and implies that pre-optimized DCE designs are generally inefficient; they have to be a compromise for everyone. Individually adaptive DCE designs, in contrast, use respondents’ previous answers to optimize the DCE design and have the potential to overcome this limitation. The aim of our research was to introduce a new type of adaptive efficient DCE designs and explore its relative performance in several Monte Carlo simulation studies and in a real-life DCE (using Sawtooth Software).
Dr. Marcel Jonker has a PhD in Public Health/Epidemiology, is an assistant professor at the Erasmus School of Health Policy and Management and Erasmus Choice Modelling Centre in the Netherlands and Rubicon research scholar at the Duke Clinical Research Institute in the United States, where he is currently working on the development and application of DCE methods in small patient samples.
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