Mammography is the dominant method of initial screening for breast cancer in women. Early detection can be crucial in saving both lives and medical costs. However, mammograms have their own costs, which come in large part from the incidences of "false positives"- the mammograms are not conclusively negative. These women are then asked to undergo further tests that involve two different types of costs: utilization tests, which involve using real resources from the tests themselves; and the psychologically adverse impacts on these women. The chances that women who receive regular mammograms will obtain a false positive are significant-up to 10 percent fall into the false negative category. Given these costs, the medical profession has considered reducing its recommendations for the use of mammograms. This may limit the effectiveness of mammograms in reducing breast cancer. Thus, examining the sources of false positives may be crucial to extending the effective use of mammography, and reducing the incidence of breast cancer. This research project proposes to examine the incidence of false negative mammograms using detection controlled estimation (DCE), an advanced econometric technique. DCE has been used in the detection of regulatory and economic events such as environmental regulation enforcement and determining tax evasion. It is designed specifically to factor out those elements that affect the underlying condition from those that affect its inspection. DCE will be employed with an extensive database from a large hospital- based mammography program in a medium-sized southern city to answer these questions. The results of the initial DCB estimation will be validated using a second DCB estimation on follow-up data from further tests on the same women. This project would appear to be the first opportunity to use follow-up data to validate DCE results.