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Discrete Choice Experiment (DCE) as a Tool to Elicit Patient Preferences in a Complex Benefit-Risk Evaluation: A Case
Jian-Yu E1, Shahrul Mt-Isa2, Kimberley Hockley3
1Epidemiology & Benefit-Risk Evaluation, Sanofi, Cambridge, MA, USA. Jian-Yu.E@sanofi.com.
Background And Objectives:
Discrete choice experiment (DCE) is increasingly recognized for its utility to elicit patient preferences in benefit-risk (BR) evaluation, particularly when options entail complex tradeoffs. This rimonabant case study focused on the methodology and versatility of DCE to elicit patient preferences for anti-obesity drugs. This study aimed to demonstrate the application of DCE as a tool to capture patient preferences concerning the BR profile of rimonabant, focusing on attribute-based choices, preference elicitation, experimental design, quantitative analysis, and practical application.
Methods:
A DCE was conducted involving standard steps: identifying attributes and levels, constructing choice sets, designing the questionnaire, and analyzing responses. Two benefit and three risk attributes important to the BR profile of anti-obesity drugs were selected based on their significance to patients from published literature. Responses were analyzed using probit regression to quantify the preference for each attribute. Preference weights, derived from DCE, were combined with efficacy and safety data from clinical trials to estimate the utility function and probability of treatment selection. Lastly, the generated marginal rates of substitution (MRS) were used to present the extent to which patients were willing to trade off benefits to avoid risks.
Results:
The coefficients from the probit model indicated that psychiatric conditions (i.e., mild anxiety to severe depression) significantly impacted treatment preference, followed by cardiovascular conditions, weight loss, and gastrointestinal conditions. On average, rimonabant was preferred over placebo 67% of the time. On the basis of MRS, to avoid a 1% increase of psychiatric condition, patients would be willing to forgo a 3.9% reduction in number of people achieving 10% weight loss, or a 0.44 level of improvement in high-density lipoprotein cholesterol, which elucidates how patients value benefits and risks when deciding on a treatment.
Conclusions:
This case study reaffirmed the utility of DCE as a valuable tool for BR evaluations, which aligned with what patients deemed important. The case study demonstrated essential attributes for anti-obesity medications. The methodological rigor and flexibility of DCE provided a robust framework for understanding, quantifying and analyzing preferences. The integration of elicited patient preferences, utility functions, and relevant clinical data offered quantitative insights into the structured BR assessment for regulatory decision-making and beyond.
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