Best-worst scaling (BWS), also known as maximum-difference scaling, is a multiattribute approach to measuring preferences. BWS aims at the analysis of preferences regarding a set of attributes, their levels or alternatives. It is a stated-preference method based on the assumption that respondents are capable of making judgments regarding the best and the worst (or the most and least important, respectively) out of three or more elements of a choice-set. As is true of discrete choice experiments (DCE) generally, BWS avoids the known weaknesses of rating and ranking scales while holding the promise of generating additional information by making respondents choose twice, namely the best as well as the worst criteria. A systematic literature review found 53 BWS applications in health and healthcare. This article expounds possibilities of application, the underlying theoretical concepts and the implementation of BWS in its three variants: ‘object case’, ‘profile case’, ‘multiprofile case’. This paper contains a survey of BWS methods and revolves around study design, experimental design, and data analysis. Moreover the article discusses the strengths and weaknesses of the three types of BWS distinguished and offered an outlook. A companion paper focuses on special issues of theory and statistical inference confronting BWS in preference measurement.

Objective Lung cancer is a major cause of cancer-related deaths and thus represents a global health problem. According to World Health Organization (WHO) estimates, approximately 1.37 million people die each year from lung cancer. Different therapeutic approaches as well as several treatment options exist. To date decisions on which therapies to use have largely been made by clinical experts. Comparative preference studies show that underlying weighting of treatment goals by experts is not necessarily congruent with the preferences of affected patients. Aim and methods The aim of this empirical study was to ascertain patient preferences in relation to treatment of non-small-cell lung cancer (NSCLC). After identification of patient-relevant treatment attributes via literature review and qualitative interviews(ten) a discrete-choice experiment including seven patient-relevant attributes was conducted using a fractional factorial NGene-design. Statistical data analysis was performed using latent class models. Results The qualitative part of this study identified outcome measures related to efficacy, side effects and mode of administration. A total of 211 NSCLC patients (N = 211) participated in the computer-assisted personal interview. A clear preference for an increase in “progression-free survival” (coef.: 1.087) and a reduction of “tumor-associated symptoms”(cough, shortness of breath and pain); coef.: 1.090) was demonstrated, followed by the reduction of side effects: “nausea and vomiting” (coef.: 0.605); “rash” (coef.: 0.432); “diarrhea” (coef.: 0.427); and, “tiredness and fatigue” (coef.: 0.423). The “mode of administration” was less important for participants (coef.: 0.141). Conclusion Preference measurement showed “progression-free survival” and “tumor-associated symptoms” had a significant influence on the treatment decision. Subgroup analysis revealed that the importance of “progression-free survival” increases with increased therapy experience. Based on the presented results therapies can be designed, assessed and chosen on the basis of patient-oriented findings. As such, more effective and efficient care of patients can be achieved and benefits increased.

For optimal solutions in health care, decision makers inevitably must evaluate trade-offs, which call for multi-attribute valuation methods. Researchers have proposed using best-worst scaling (BWS) methods which seek to extract information from respondents by asking them to identify the best and worst items in each choice set. While a companion paper describes the different types of BWS, application and their advantages and downsides, this contribution expounds their relationships with microeconomic theory, which also have implications for statistical inference. This article devotes to the microeconomic foundations of preference measurement, also addressing issues such as scale invariance and scale heterogeneity. Furthermore the paper discusses the basics of preference measurement using rating, ranking and stated choice data in the light of the findings of the preceding section. Moreover the paper gives an introduction to the use of stated choice data and juxtaposes BWS with the microeconomic foundations.