Abstract
This article aims to describe the conjoint analysis (CA) method and its application in healthcare settings, and to provide researchers with a brief guide to conduct a conjoint study. CA is a method for eliciting patients’ preferences that offers choices similar to those in the real world and allows researchers to quantify these preferences. To identify literature related to conjoint analysis, a comprehensive search of PubMed (MEDLINE), EMBASE, Web of Science, and Google Scholar was conducted without language or date restrictions. To identify the trend of publications and citations in conjoint analysis, an online search of all databases indexed in the Web of Science Core Collection was conducted on the 8th of December 2021 without time restriction. Searching key terms covered a wide range of synonyms related to conjoint analysis. The search field was limited to the title, and no language or date limitations were applied. The number of published documents related to CA was nearly 900 during the year 2021 and the total number of citations for CA documents was approximately 20,000 citations, which certainly shows that the popularity of CA is increasing, especially in the healthcare sciences services discipline, which is in the top five fields publishing CA documents. However, there are some limitations regarding the appropriate sample size, quality assessment tool, and external validity of CA.
Highlights
The popularity of conjoint analysis (CA) in health outcomes research has been increasing in recent years [1,2]
The untraditional concept of this research method is still unclear for many healthcare researchers and clinicians in terms of the design complexity and the absence of confirmed sample size [1,3,4]
The popularity of CA in healthcare has been increasing and its use in this setting is gradually competing with its use in business and marketing research
Summary
The popularity of conjoint analysis (CA) in health outcomes research has been increasing in recent years [1,2]. Approaches that are referred to as stated and revealed preference methods have been executed to assess patients’ preferences in relation to drug pharmacodynamics, pharmacokinetics, and financial characteristics [5,6]. The stated preferences method relies on what people state while evaluating alternative hypothetical situations (hypothetical decision) [7,8]. The revealed preferences method relies on the observations of the actual choices made by people to measure preferences (actual decision) [7,8,9]. The application of alternative methods in clinical practice is linked to the patients’ own perspective of selecting the best cost-effective treatment, while considering their social and psychological situation instead of relying mostly on disease symptoms [6,11,12]
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