American Statistical Association
Diagnostic and prognostic models are typically evaluated with measures of accuracy such as sensitivity, specificity, and area under the curve. Such measures often tell us little or nothing about a models clinical value, such as its impact on number of cancers found or unnecessary biopsies avoided. Decision-analytic techniques may allow assessment of clinical outcomes but they generally require collection of additional information, such as patient preferences or costs of treatment. We developed a novel method decision curve analysis to overcome the drawbacks of traditional statistical and decision analysis.
In this talk, I will:
|Date:||Wednesday, February 28, 2007|
|Time:||4:00 P.M. - 5:00 P.M.|
Memorial Sloan-Kettering Cancer Center
Department of Epidemiology and Biostatistics
307 East 63rd Street
(between First and Second Avenues)
3rd Floor Conference Room
New York, New York
Note: To gain access to the building, please follow the directions by the telephone in the foyer.