Seven statistical models showed that both screening mammography and treatment have helped reduce the rate of death from breast cancer in the United States.

No abstract

This paper proposes a new method for planning randomized clinical trials. This method is especially suited to comparison of a best standard or control treatment with an experimental treatment. Patients are allocated into two groups by a random or chance mechanism. Patients in the first group receive standard treatment; those in the second group are asked if they will accept the experimental therapy; if they decline, they receive the best standard treatment. In the analyses of results, all those in the second group, regardless of treatment, are compared with those in the first group. Any loss of statistical efficiency can be overcome by increased numbers. This experimental plan is indeed a randomized clinical trial and has the advantage that, before providing consent, a patient will know whether an experimental treatment is to be used.

Background Despite trials of mammography and widespread use, optimal screening policy is controversial. Design and Objective Six models use common data elements to evaluate US screening strategies. Data Sources The models use national data on age-specific incidence, competing mortality, mammography characteristics and treatment effects. Target Population and Time Horizon A contemporary population cohort followed over their lifetimes. Perspective We use a societal perspective for analysis. Interventions We evaluate 20 screening strategies with varying initiation and cessation ages applied annually or biennially. Outcome Measures Number of mammograms, breast cancer mortality reduction or life years gained [LYG] (vs. no screening), false positives, unnecessary biopsies and over-diagnosis. Results of Base Case The 6 models produce consistent rankings of screening strategies. Screening biennially maintains an average of 81% (range across strategies and models 67–99%) of the benefit of annual screening with almost half the number of false positives. Screening biennially from ages 50 to 69 achieves a median 16.5% (range 15%–23%) breast cancer mortality reduction vs. no screening. Initiating biennial screening at age 40 (vs. 50) reduces mortality by an additional 3% (range 1%–6%), consumes more resources and yields more false positives. Biennial screening after age 69 yields some additional mortality reduction in all models but over-diagnosis increases most substantially at older ages. Sensitivity Analysis Results Varying test sensitivity or treatment patterns do not change conclusions. Limitations Results do not include morbidity from false positives, knowledge of earlier diagnosis or under-going 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.