Breast cancer patients' attitudes about rationing postlumpectomy radiation therapy: applicability of trade-off methods to policy-making.

Purpose: Along with evidence, clinical policies must take patients' values into account. Particularly where evidence is limited and where assumptions of utility-maximizing behavior may not be valid, new methods such as trade-off techniques (TOTs), which allow elicitation of patients' treatment alternatives, might be useful in policy formulation. We used TOTs to assess breast cancer patients' attitudes toward two clinical policies designed to ration adjuvant postlumpectomy breast radiation therapy.
Methods: Cross-sectional interviews were performed in a tertiary cancer center. A total of 102 patients were presented with information about the side effects and benefits associated with two hypothetical decisions: (1) willingness to receive treatment elsewhere to shorten the wait for radiation therapy, and (2) foregoing radiation therapy in the face of small marginal benefits. For each scenario, a TOT was used to identify the maximal acceptable wait time (MAWT) for therapy and the benefit threshold at which the patient would forego therapy. Associations of clinical and demographic factors with these decisions were determined by regression analysis.
Results: Patients would be willing to wait, on average, 7 weeks before wanting to leave their city for radiation therapy, less than the 13-week delay our patients actually faced. Older patients were less willing to wait (P = .013); 46% of patients would not give up radiation therapy, even in the face of no stated benefit. Willingness to give up radiation therapy was predicted by willingness to accept delay (odds ratio [OR], 1.84; 95% confidence interval [CI], 1.05 to 3.37) and being employed (OR, 2.61; 95% CI, 1.08 to 6.54). Patients with larger tumors were less willing to give up radiation therapy (OR, 0.57; 95% CI, 0.31 to 0.97).
Conclusion: Even in difficult decisions such as rationing postlumpectomy breast cancer radiation therapy, TOTs can inform policy formulation by indicating the distributions of patients' preferences.