Presentation (CTI)

OBJECTIVES: Patient preference studies are increasingly used to inform healthcare decision making, with discrete choice experiments (DCEs) being the most widely applied method. However, DCEs may be less suitable for small-sample settings due to their sample size requirements, cognitive burden, and analytical complexity. Without appropriate methodological alternatives, researchers risk generating biased or non-robust evidence. This study aims to provide an evaluation of four different stated preference methods [DCE, Best-Worst Scaling (BWS), Multidimensional Thresholding (MDT), and Online Personal Utility Functions (OPUF)] in small sample contexts.
METHODS: We conducted a purposive review of the literature to examine four methods, focusing on: methodological features, robustness, and relevance to small-sample preference studies. Articles were identified through targeted searches of PubMed/Medline, Scopus and Google Scholar. Abstracts were screened to select relevant articles to the review objective. Drawing on prior literature and emerging empirical evidence, we provide a critical comparison of each method and their capacity to yield reliable and interpretable results in preference studies with limited sample sizes.
RESULTS: There is no one-size-fits-all preference elicitation method in small sample contexts. Each approach offers unique advantages and limitations depending on the research context. Method selection should be guided by study design, sample size, participant characteristics, cognitive burden, and the need for exploration of preference heterogeneity. While DCE and BWS are well-established with extensive precedent in the literature, newer methods such as MDT and OPUF show promise, particularly in small samples. However, their use is still emerging, and further research is needed to strengthen the validity and robustness of these innovative approaches.
CONCLUSIONS: This study recommends that the choice of method adopted should be tailored to the specific research objectives and study characteristics. Research suggests that BWS is a robust alternative method of eliciting preferences in small samples, while OPUF and MDT appear promising but require further validation.