American Statistical Association
ROC curves are routinely used to express the discriminatory power of a marker in terms of sensitivity and specificity. Corresponding methods for positive and negative predictive values are less commonly used, possibly because they are functions of the prevalence. In this talk, I will discuss a method to decompose the predictive values as a product of two functions: one involving prevalence and the other involving the cumulative distribution of the marker conditional on the gold standard. This decomposition lends itself to a graphical summary and it can also be used to model the predictive values as a function of covariates. I will use examples from the literature as well as using MSK data to illustrate various uses of this decomposition.
|Date:||Wednesday, April 14, 2010|
|Time:||4:00 - 5:00 P.M.|
Memorial Sloan-Kettering Cancer Center
Department of Epidemiology and Biostatistics
307 East 63rd Street
(between First and Second Avenues)
New York, New York
Note: To gain access to the building, please follow the directions by the telephone in the foyer.