Applied Statistics - L.J. Wei and Tianxi Cai 

 This week the Applied Statistics Workshop will present a talk by L.J. Wei and Tianxi Cai of the Department of Biostatistics at the Harvard School of Public Health.  Professor Wei received his Ph.D. in statistics from the University of Wisconsin at Madison and has served on the faculty of several universities before coming to Harvard in 1991.  Professor Cai received her Sc.D. from the Harvard School of Public Health in 1999 and was a faculty member at the University of Washington before returning to HSPH in 2002. Professors Wei and Cai will present a talk entitled " Evaluating Prediction Rules for t-Year Survivors With Censored Regression Models ." The presentation will be at noon on Wednesday, April 5 in Room N354, CGIS North, 1737 Cambridge St. Lunch will be provided. The abstract of the paper follows on the jump: 
 


 
Suppose that we are interested in establishing simple, but reliable rules for predicting future t-year survivors via censored regression models. In this article, we present inference procedures for evaluating such binary classification rules based on various prediction precision measures quantified by the overall misclassification rate, sensitivity and specificity, and positive and negative predictive values. Specifically, under various working models we derive consistent estimators for the above measures via substitution and cross validation estimation procedures. Furthermore, we provide large sample approximations to the distributions of these nonsmooth estimators without assuming that the working model is correctly specified. Confidence intervals, for example, for the difference of the precision measures between two competing rules can then be constructed. All the proposals are illustrated with two real examples and their finite sample properties are evaluated via a simulation study.