Discrete Choice Experiments in Health Economics: Past, Present and Future

Abstract

Objectives Discrete choice experiments (DCEs) are increasingly advocated as a way to quantify preferences for health. However, increasing support does not necessarily result in increasing quality. Although specific reviews have been conducted in certain contexts, there exists no recent description of the general state of the science of health-related DCEs. The aim of this paper was to update prior reviews (1990–2012), to identify all health-related DCEs and to provide a description of trends, current practice and future challenges. Methods A systematic literature review was conducted to identify health-related empirical DCEs published between 2013 and 2017. The search strategy and data extraction replicated prior reviews to allow the reporting of trends, although additional extraction fields were incorporated. Results Of the 7877 abstracts generated, 301 studies met the inclusion criteria and underwent data extraction. In general, the total number of DCEs per year continued to increase, with broader areas of application and increased geographic scope. Studies reported using more sophisticated designs (e.g. D-efficient) with associated software (e.g. Ngene). The trend towards using more sophisticated econometric models also continued. However, many studies presented sophisticated methods with insufficient detail. Qualitative research methods continued to be a popular approach for identifying attributes and levels. Conclusions The use of empirical DCEs in health economics continues to grow. However, inadequate reporting of methodological details inhibits quality assessment. This may reduce decision-makers’ confidence in results and their ability to act on the findings. How and when to integrate health-related DCE outcomes into decision-making remains an important area for future research. Electronic supplementary material The online version of this article (10.1007/s40273-018-0734-2) contains supplementary material, which is available to authorized users.

Consumer preference to utilise a mobile health app: A stated preference experiment

“…The survey was administered in a sample of 500 Australian adults, representative of the general population in terms of age and gender defined by the Australian Bureau of Statistics (ABS) Consumer preference and mobile health app [31]. A recent review of the field suggested that a sample size of 500 was typical for these kinds of studies (which had a median of 401) [32]. There were also screening questions that only accepted respondents who own a smartphone and attend an appointment at a medical centre annually.…”

Adolescent and parent perspectives on the acceptability of financial incentives to promote self‐care in adolescents with type 1 diabetes

“…Thus, there remains a need to test different structures for a T1D FI program to determine the ideal value and structure of incentives to promote meaningful behavior change in a cost‐effective manner. Discrete choice experiments offer a methodology in which adolescents can compare the motivational value of dozens of possible incentive structures, thus strengthening the economic theory behind the design of future FI programs …”

Patient preferences for attributes of primary surgical debulking versus neoadjuvant chemotherapy for treatment of newly diagnosed ovarian cancer

“…A DCE is a stated‐preference survey method used to quantify preferences for attributes of medical treatments, health states, or other services . Participants choose between pairs of alternative constructed treatment profiles characterized by specific levels of each attribute.…”

“…A DCE is a stated-preference survey method used to quantify preferences for attributes of medical treatments, health states, or other services. 23,24 Participants choose between pairs of alternative constructed treatment profiles characterized by specific levels of each attribute. Repeated choices between treatment profiles based on an experimental design with known statistical properties provide preference data to statistically estimate trade-off rates among the included attributes.…”

“…Importance weights summing to 100 are presented in Supporting Figure 3. OS was the most important attribute to respondents, with an importance weight of 36 (95% CI, 30-42), followed by complications requiring readmission (23; 95% CI, 18-27), PFS (19; 95% CI, [15][16][17][18][19][20][21][22][23][24], surgical mortality (16; 95% CI, [11][12][13][14][15][16][17][18][19][20][21], extent of surgery (4; 95% CI, 1-6), and treatment order (2; 95% CI, 0.2-5). Figures 3 and 4 display risk equivalents and benefit equivalents, respectively.…”