Effective sample size for computing prior hyperparameters in Bayesian phase I-II dose-finding.

BACKGROUND: The efficacy-toxicity trade-off based design is a practical Bayesian phase I-II dose-finding methodology. Because the design's performance is very sensitive to prior hyperparameters and the shape of the target trade-off contour, specifying these two design elements properly is essential. PURPOSE: The goals are to provide a method that uses elicited mean outcome probabilities to derive a prior that is neither overly informative nor overly disperse, and practical guidelines for specifying the target trade-off contour. METHODS: A general algorithm is presented that determines prior hyperparameters using least squares penalized by effective sample size. Guidelines for specifying the trade-off contour are provided. These methods are illustrated by a clinical trial in advanced prostate cancer. A new version of the efficacy-toxicity program is provided for implementation. RESULTS: Together, the algorithm and guidelines provide substantive improvements in the design's operating characteristics. LIMITATIONS: The method requires a substantial number of elicited values and design parameters, and computer simulations are required to obtain an acceptable design. CONCLUSION: The two key improvements greatly enhance the efficacy-toxicity design's practical usefulness and are straightforward to implement using the updated computer program. The algorithm for determining prior hyperparameters to ensure a specified level of informativeness is general, and may be applied to models other than that underlying the efficacy-toxicity method.

Effects of mammography screening under different screening schedules: model estimates of potential benefits and harms.

BACKGROUND: Despite trials of mammography and widespread use, optimal screening policy is controversial. OBJECTIVE: To evaluate U.S. breast cancer screening strategies. DESIGN: 6 models using common data elements. DATA SOURCES: National data on age-specific incidence, competing mortality, mammography characteristics, and treatment effects. TARGET POPULATION: A contemporary population cohort. TIME HORIZON: Lifetime. PERSPECTIVE: Societal. INTERVENTIONS: 20 screening strategies with varying initiation and cessation ages applied annually or biennially. OUTCOME MEASURES: Number of mammograms, reduction in deaths from breast cancer or life-years gained (vs. no screening), false-positive results, unnecessary biopsies, and overdiagnosis. RESULTS OF BASE-CASE ANALYSIS: The 6 models produced consistent rankings of screening strategies. Screening biennially maintained an average of 81% (range across strategies and models, 67% to 99%) of the benefit of annual screening with almost half the number of false-positive results. Screening biennially from ages 50 to 69 years achieved a median 16.5% (range, 15% to 23%) reduction in breast cancer deaths versus no screening. Initiating biennial screening at age 40 years (vs. 50 years) reduced mortality by an additional 3% (range, 1% to 6%), consumed more resources, and yielded more false-positive results. Biennial screening after age 69 years yielded some additional mortality reduction in all models, but overdiagnosis increased most substantially at older ages. RESULTS OF SENSITIVITY ANALYSIS: Varying test sensitivity or treatment patterns did not change conclusions. LIMITATION: Results do not include morbidity from false-positive results, patient knowledge of earlier diagnosis, or unnecessary treatment. CONCLUSION: Biennial screening achieves most of the benefit of annual screening with less harm. Decisions about the best strategy depend on program and individual objectives and the weight placed on benefits, harms, and resource considerations. PRIMARY FUNDING SOURCE: National Cancer Institute.

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.