Li Chen; Assistant Professor,  Markey Cancer Center and the Department of Biostatistics



Sept 20th, 4-5 p.m.



MDS 223



Refreshments: 3:30-4:00



312 MDS building

    

We propose a graphical measure, the negative predictive function, to quantify the predictive accuracy of covariates for survival outcomes. This new measure characterizes the survival probabilities over time conditional on a thresholded linear combination of covariates and has direct clinical utility. We show that this function is maximized at the set of covariates truly related to event times and thus can be used to compare the predictive accuracy of different sets of covariates. We construct nonparametric estimators for this function under right censoring and prove that the proposed estimators, upon proper normalization, converge weakly to zero-mean Gaussian processes. To bypass the estimation of complex density functions involved in the asymptotic variances, we adopt the bootstrap approach and establish its validity. Simulation studies demonstrate that the proposed methods perform well in practical situations. A breast cancer gene expression study is provided for illustration.