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Preferences of patients with metastatic breast cancer.

6044 Background: Patients with metastatic breast cancer face difficult trade-offs between toxicity and efficacy. Techniques from other fields can help analyze trade-off preferences.


METHODS
The Research Advocacy Network and CBWhite conducted research using conjoint analysis (CA) as part of the DOD Center of Excellence for Individualization of Therapy in Breast Cancer. An online survey was released via three organizations: Living Beyond Breast Cancer, Metastatic Breast Cancer Network, and BCMets listserv. Over 400 women answered questions about biomarker tests and questions designed to elicit views on the trade-off between benefit and side effect. CA questions present pairs of hypothetical treatments, each defined by benefit and side effect likelihoods. In each question, respondents select a preferred treatment or no treatment. Analysis of patterns provides results allowing prediction of selecting treatment for any combination of benefit and side effect.


RESULTS
Preference curves show greater preference for higher benefit and lower side effect likelihoods. Range is from 92% for "best" combination (60% benefit/20% side effect) to 12% for "worst" combination (10% benefit/90% side effect). Benefit appears more influential than side effect; selecting treatment drops more quickly as benefit diminishes and more slowly as side effect increases. Subgroup analysis shows differences depending on age, side effect, and presence of children. Using CA to predict impact of biomarkers shows: 19% to 27% may change treatment decision if biomarker predicts 30% vs 50% benefit likelihood;10% to 18% may change treatment decision if biomarker predicts 30% vs 50% side effect likelihood.


CONCLUSIONS
Conjoint analysis can be used to quantify patient preference with respect to benefit and side effect trade-offs. Predictions and usefulness will be improved by designing CA based on specific treatment research questions that have particular side effect profiles. Biomarker influence can be modeled using conjoint data. [Table: see text].