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

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 pertai...

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Bibliographic Details
Published in:Statistics in medicine 2016-07, Vol.35 (15), p.2543-2560
Main Authors: Jaynes, Jessica, Wong, Weng-Kee, Xu, Hongquan
Format: Article
Language:English
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
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Summary: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.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.6882