Evaluating Prediction Rules for t-Year Survivors With Censored Regression Models

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. Evaluating Prediction Rules for t-Year Survivors with Censored Regression Models By Hajime Uno Division of Biostatistics, School of Pharmaceutical Sciences, Kitasato University, Tokyo, Japan, 108-8641 [email protected] Tianxi Cai Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115 [email protected] Lu Tian Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611 [email protected] and L. J. Wei Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115 [email protected]