Using blocked fractional factorial designs to construct discrete choice experiments for healthcare studies

• Published in: Statistics in medicine Vol. 35; no. 15; pp. 2543 - 2560
• Main Authors: Jaynes, Jessica, Wong, Weng-Kee, Xu, Hongquan
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 10.07.2016; Wiley Subscription Services, Inc
• Subjects: aliasing; blocked fractional factorial designs; Choice Behavior; Decision Making; Delivery of Health Care; Design of experiments; Factorial experiments; Humans; interaction effects; locally optimal designs; Medical research; multinomial logit model; Patient Preference; Research Design; Research methodology; multinomial logit model; blocked fractional factorial designs; interaction effects; aliasing; locally optimal designs
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.6882

Discrete choice experiments (DCEs) are increasingly used for studying and quantifying subjects preferences in a wide variety of healthcare 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 healthcare and identification of key attributes affecting healthcare. 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 healthcare studies to evaluate (i) asthma patients' preferences for symptom‐based outcome measures and (ii) patient preference for breast screening services. Copyright © 2016 John Wiley & Sons, Ltd.