1. BACKGROUND AND OBJECTIVES
ASSESSING PREFERENCES IN DISCRETE CHOICE EXPERIMENTS (DCEs): EFFECTS OF THE APPLIED DESIGN ON THE STUDY RESULTS
Sabrina Mueller1, Sabine Bauer1, Thomas Wilke1
1Ingress-Health HWM GmbH, Wismar, Germany
BACKGROUND AND OBJECTIVES
Recent years have seen an increased level of interest in the definition and valuation of patients’ preferences in health care and several methods have been applied. Among them were simple rating approaches, which have the disadvantage that patients’ rating behavior mostly showed that patients would like to have all benefits but none of the indirect or direct costs. Thus, more complex forms of preference eliciting techniques, like conjoint-analysis methods, have become increasingly popular. Especially, discrete-choice experiments (DCEs) have become a widely accepted approach in health care. In a DCE, participants are presented with descriptions of two (or more) complete hypothetical treatment options based on a combination of different characteristics and asked to select their preferred treatment alternative. Within the procedure of the development of a DCE design, the definition of the attributes and respective levels is of utmost importance. But there are also several further design aspects that need to be considered when generating choice tasks for a DCE. However, little is known about how these options influence the outcomes of a DCE study.
METHODS
A cross-sectional survey of 122 students at a German University was conducted, investigating preferences of students with regard to a hypothetical health insurance bonus program. The attributes and related levels to describe different alternatives of such a health insurance bonus program were defined as follows (basic design):
Basic design
1. Sports achievement certificate
"No certificate necessary" versus
- "At least membership in a fitness club"
2. Professional tooth cleaning
“Not necessary“ versus
"At least twice per year"
3. Assessment of the Body-Mass-Index (BMI)
"Not necessary“ versus
"BMI needs to be within a normal range based on physician’s assessment“
4. Participating in a health coaching
"Online-training: 4 x 2 hours per year"
5. Pay-back bonus
"30.- € per year“ versus
“60.- € per year"
In different conditional logit regression models, the influence of different attributes on the probability of a respondent choosing a specific program option was estimated. Based on this, the DCE outcomes of the different designs (BD vs. M1, M2, M3) were compared.
Modification of choice tasks
This basic design was modified three-times as follows:
Modification 1 (M1): The number of levels for the attribute “tooth cleaning” was increased to three by adding the additional level “at least once per year”. Respondents may tend to give more importance to attributes with more levels.
Modification 2 (M2): The order of the attributes on choice cards was changed. Respondents may tend to give more importance to attributes that are listed/described first.
Modification 3 (M3): An opt-out option was added. There seems to be consensus regarding the inclusion of an opt-out option in DCEs that aim to determine the potential participation in an elective program as such an option is more in accordance with the respondent's choice options in real life. However, if individual preferences are measured to determine which components define the most preferred program, the inclusion of an opt-out option might not be a necessity but rather a threat to efficiency.
Consequently, each respondent completed a questionnaire with three sets of choice tasks with eight different choices each (one basic design (BD) and two further sets with design modifications).
RESULTS
Both the effect size and even the direction of the influence of the attributes on respondents’ utility varied between the design options. So, as an example, the most important attribute in the basic design was the mandatory tooth cleaning (relative importance: 65%), but the relative importance decreased to 38% when M2 was applied. Furthermore, the attribute “sports achievement certificate” showed a considerable effect on student’s decision, but showed hardly any effect in all three DCE design modification. Student’s decision in the DCE including an opt-out option was almost only driven by the attribute “Professional tooth cleaning”.
Figure 1 – Relative importance of the attributes resulting from the different DCE designs applied
CONCLUSION
If DCEs are used to inform health policy decision makers, it is crucial that presented results are valid and robust. Obviously, DCE design decisions may substantially influence outcomes of DCE studies. We recommend to take this aspect into account when designing a DCE study. Future research should empirically explore how choice sets should be presented to make them as unbiased as possible.
KEYWORDS
REFERENCES
Pfarr C, Schmid A, Schneider U. Using discrete choice experiments to understand preferences in health care. Dev Health Econ Public Policy. 2014;12:27-48
Veldwijk J, Lambooij MS, de Bekker-Grob EW, Smit HA, de Wit GA. The effect of including an opt-out option in discrete choice experiments. PLoS One Nov 3;9(11):e
Ryan M, Gerard K. Using discrete choice experiments to value health care programmes: current practice and future research reflections. Appl Health Econ Health Policy. 2003;2(1):55-64.
Patient Preference, Discrete Choice Experiment, DCE, Design effects