Do Patient Preferences Align With Value Frameworks? A Discrete-Choice Experiment of Patients With Breast Cancer

Abstract

Purpose. Assess patient preferences for aspects of breast cancer treatments to evaluate and inform the usual assumptions in scoring rubrics for value frameworks. Methods. A discrete-choice experiment (DCE) was designed and implemented to collect quantitative evidence on preferences from 100 adult female patients with a self-reported physician diagnosis of stage 3 or stage 4 breast cancer. Respondents were asked to evaluate some of the treatment aspects currently considered in value frameworks. Respondents’ choices were analyzed using logit-based regression models that produced preference weights for each treatment aspect considered. Aggregate- and individual-level preferences were used to assess the relative importance of treatment aspects and their variability across respondents. Results. As expected, better clinical outcomes were associated with higher preference weights. While life extensions with treatment were considered to be most important, respondents assigned great value to out-of-pocket cost of treatment, treatment route of administration, and the availability of reliable tests to help gauge treatment efficacy. Two respondent classes were identified in the sample. Differences in class-specific preferences were primarily associated with route of administration, out-of-pocket treatment cost, and the availability of a test to gauge treatment efficacy. Only patient cancer stage was found to be correlated with class assignment ( P = 0.035). Given the distribution of individual-level preference estimates, preference for survival benefits are unlikely to be adequately described with two sets of preference weights. Conclusions. Although value frameworks are an important step in the systematic evaluation of medications in the context of a complex treatment landscape, the frameworks are still largely driven by expert judgment. Our results illustrate issues with this approach as patient preferences can be heterogeneous and different from the scoring weights currently provided by the frameworks.