A Novel Approach to Computing Preference Estimates for Different Treatment Pathways: An Application in Oncology

Area of Science:

• Oncology
• Health Services Research
• Decision Science

Background:

• Cancer patients often undergo sequential treatments with varying side effects.
• Treatment pathways can be fixed or flexible, allowing for adjustments based on outcomes.
• A methodology is needed to estimate patient preferences for entire treatment sequences.

Purpose of the Study:

• To develop a novel methodology for estimating patient preferences across complex, sequential cancer treatment pathways.
• To assess patient preferences for key attributes of early breast cancer treatment pathways.

Main Methods:

• An online discrete choice experiment was conducted with early breast cancer patients.
• Hierarchical Bayesian modeling calculated attribute-level preference weights.
• Pathway preferences were estimated by summing time-adjusted weights for efficacy, pathway flexibility, duration, administration, and adverse events.

Main Results:

• Serious adverse event risk was the most significant factor in treatment pathway preferences.
• Patients showed a strong preference for flexible over fixed treatment pathways.
• Increased efficacy and decreased pathway duration also positively influenced preferences.

Conclusions:

• A new methodology enables comprehensive patient preference assessment for sequential cancer treatments.
• This approach facilitates the comparison of complex treatment pathways based on patient values.