Discrete choice experiments (DCEs) are increasingly used for studying and quantifying subjects preferences in a wide variety of health care applications. They provide a rich source of data to assess real-life decision making processes, which involve trade-offs between desirable characteristics pertaining to health and health care, and identification of key attributes affecting health care.The choice of the design for a DCE is critical because it determines which attributes’ effects and their interactions are identifiable. We apply blocked fractional factorial designs to construct DCEs and address some identification issues by utilizing the known structure of blocked fractional factorial designs. Our design techniques can be applied to several situations including DCEs where attributes have different number of levels. We demonstrate our design methodology using two health care studies to evaluate (1) asthma patients’ preferences for symptom-based outcome measures, and (2) patient preference for breast screening services.
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