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Activity Number: 145
Type: Invited
Date/Time: Monday, August 1, 2016 : 10:30 AM to 12:20 PM
Sponsor: Health Policy Statistics Section
Abstract #318063 View Presentation
Title: Tailoring Decision Making to Reflect Patient Preferences and Expected Treatment Outcomes
Author(s): Megan S. Schuler* and Laura Anne Hatfield
Companies: Harvard Medical School and Harvard Medical School
Keywords: latent class analysis ; health utilities ; shared decision-making

Medical decision making is a complex process that includes many competing factors. With the rise of patient-centered care, patient preferences are increasingly incorporated into the decision process. The field of oncology, especially, has adopted a shared decision making framework, with patient preferences increasingly influencing decisions between surgery, chemotherapy, and radiation. Preferences for a given set of health states are commonly assessed using health utilities, typically measured on a continuous scale from 0, representing death, to 1, representing perfect health. While relatively few studies have focused on preference heterogeneity across individuals, the existing studies in this area have consistently demonstrated variability in how individuals value health states (e.g., some individuals prefer the most aggressive treatment, while others may prefer to trade survival time for reduced side effects). We use latent class analysis to identify distinct subgroups of individuals with respect to health state preferences among colon cancer patients. Additionally, we discuss a statistical framework that accounts for preference heterogeneity when tailoring treatment choices.

Authors who are presenting talks have a * after their name.

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