Modeling the impact of treatment and screening on U.S. breast cancer mortality: a Bayesian approach.
BACKGROUND: Breast cancer mortality (BCM) in the United States declined from 33.1 per 100,000 women in 1990 to 26.6 per 100,000 women in 2000, yielding a 19.6% relative decline in BCM since 1990. Our goal is to apportion this decline between screening and therapy and to be able to state with some certainty that these interventions affected this decline. METHODS: We started with an age-appropriate population of 2,000,000 women in 1975 and monitored these women through 2000. On the basis of population data each year, we assigned screening and breast cancer to women. If a woman was diagnosed with breast cancer, we simulated a lifetime for her with death from breast cancer, and we modified this lifetime depending on the use of adjuvant therapy and whether the cancer was screen-detected. A woman's lifetime was taken as the minimum of her lifetime with death from breast cancer and her simulated natural lifetime. We used Bayesian simulation modeling, which allows for associating probability distributions with our estimates. RESULTS: We calculated the probabilities that screening mammography and adjuvant therapy contributed to the observed decline in BCM to be 90% and 99%, respectively. The posterior mean reduction in BCM due to screening is 10.6% +/- 5.7% and due to therapy is 19.5% +/- 5.4%. The decrease in the hazard of BCM due to tamoxifen use for ER-positive tumors is 37% +/- 14% and that due to adjuvant (nontaxane) chemotherapy is 15% +/- 14%. DISCUSSION: The spread in our posterior distributions reflect the uncertainty present in the data sources available to us. However, despite this uncertainty we conclude a high probability that both screening and improvements in therapy contributed to the reduction in BCM observed in the United States from 1990 to 2000.