Combining patient preferences with expected treatment outcomes to inform decision-making.

Patient-centered care involves patients in decision-making about their medical care. Effective shared decision-making requires relevant evidence on the expected health outcomes of treatment, assessment of patient preferences for possible health outcomes, and a method to synthesize this information. Few tools exist to help clinicians and patients synthesize clinical evidence and personal preferences. We develop a statistical framework that combines multiple data sources on expected treatment outcomes with individual preferences to produce a personalized preference-weighted outcome score (PWOS). The PWOS can be calculated for multiple treatments and compared to determine which will provide the best balance of risks and benefits given a patient's preferences. We demonstrate our method by evaluating adjuvant chemotherapy regimens for colorectal cancer. We begin by identifying heterogeneity in patient preferences for health outcomes associated with colorectal cancer disease and treatment, finding classes of patients who value efficacy and side effects differently. Then we fit a hierarchical Bayesian model to randomized clinical trials and produce posterior distributions of expected outcomes under three chemotherapy regimens. We use the PWOS to combine distinct sets of patient preferences with these expected outcomes. Our method preserves estimation uncertainty and accounts for correlation among outcomes. In a simulation study of applying PWOS to make decisions at the population level, we find that our methods are most useful when there are large differences in individual preferences and small distinctions in treatment efficacy (i.e., preference-sensitive settings). [ABSTRACT FROM AUTHOR]