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Using blocked fractional factorial designs to construct discrete choice experiments for healthcare studies
Author(s) -
Jaynes Jessica,
Wong WengKee,
Xu Hongquan
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6882
Subject(s) - identification (biology) , construct (python library) , computer science , health care , fractional factorial design , factorial experiment , preference , factorial , econometrics , statistics , mathematics , machine learning , economics , mathematical analysis , botany , biology , programming language , economic growth
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.