Racial Variation in the Cost-Effectiveness of Chemotherapy for Prostate Cancer - Grabner, Michael (American Society of Clinical Oncology, 2011)
PURPOSE: Heterogeneity of treatment effects and expenditures impacts the cost-effectiveness of health interventions. This study investigates the variation in costs, effects, and incremental cost-effectiveness ratios (ICERs) associated with chemotherapy in elderly patients with metastatic (M1) prostate cancer (PC) across race/ethnicity subgroups (non-Hispanic whites, non-Hispanic blacks, and others). STUDY DESIGN: Retrospective observational analysis. METHODS: We examined patients age 66 years or older, identified by using the linked Surveillance, Epidemiology, and End Results–Medicare data set, who were diagnosed with M1 PC between 2000 and 2005. Cost data on the basis of Medicare reimbursements were available for 36 months after diagnosis. Mean costs and effects (life-years gained [LYG]) were adjusted for censoring. The baseline scenario examined PC-specific medical costs at 24 months and required survival of at least three months. Sensitivity analysis considered sampling uncertainty, selection into treatment, and adjustments to initial model assumptions. RESULTS: We identified 3,888 patients with M1 PC, of whom 24% (n = 930) received chemotherapy (primarily docetaxel and mitoxantrone). Twenty percent of observations were censored. The full sample ICER was $99,146 per LYG (95% CI, $75,042 to $130,195). Estimates for whites (ICER, $107,095; 95% CI, $78,391 to $148,272), blacks (ICER, $59,887; 95% CI, $22,860 to $121,509), and others (ICER, $123,909; 95% CI, $37,782 to $366,376) suggest considerable variation in the likelihood of chemotherapy being cost-effective. Results were similar in sensitivity analysis. CONCLUSION: Chemotherapy use in elderly patients with M1 PC is associated with an ICER of $99,146 per LYG. Subgroup analysis revealed heterogeneity in point estimates and considerable statistical uncertainty. To generate a reliable evidence base, efforts to increase the representation of minorities in health care data sets need to continue.