Decision analysis using individual patient preferences to determine optimal treatment for localized prostate cancer

Background: Selecting treatment for clinically localized prostate cancer remains an ongoing challenge. Previous decision analyses focused on a hypothetical patient with average preferences, but preferences differ for clinically similar patients, implying that their optimal therapies may also differ.

Methods: A decision model was constructed comparing 4 treatments for localized prostate cancer: 1) radical prostatectomy (RP); 2) external beam radiation (EB); 3) brachytherapy (BT); and 4) watchful waiting (WW). Published data were used regarding treatment success, side effects, and noncancer survival, and 156 men with prostate cancer were surveyed to elicit preferences in quality-adjusted life years (QALYs). The clinical scenarios were determined (age, tumor grade, and prostate-specific antigen [PSA]) for which variations in patient preferences led to different optimal treatments and those for which the optimal treatment was unaffected by preferences.

Results: Patient preferences were critical in determining treatment for low-risk cancers (Gleason score <or=6, PSA <or=10 ng/mL) and for patients aged 75 years and older. In younger patients with more aggressive tumors, RP and EB were always superior to WW or BT, regardless of preferences (average gain in quality-adjusted life expectancy vs WW for a 60-year-old with a medium-risk tumor = +1.4 years for RP and +1.7 for EB; for a high-risk tumor = +2.1 years for RP and +2.4 for EB). BT was a reasonable option for low-risk tumors at any age. WW was only reasonable for patients aged 70 and older with low-risk tumors or those aged 80 years and older with medium-risk tumors. Selecting treatment based on average preferences leads to suboptimal choices for 30% of patients.

Conclusions: The optimal treatment for prostate cancer depends on both the clinical scenario (patient age and tumor aggressiveness) and the patient's preferences. Decision analyses taking individualized preferences into account may be a useful adjunct in clinical decision-making.