Addressing issues associated with evaluating prediction models for survival endpoints based on the concordance statistic.

ABSTRACT: Prediction models for disease risk and prognosis play an important role in biomedical research, and evaluating their predictive accuracy in the presence of censored data is of substantial interest. The standard concordance (c) statistic has been extended to provide a summary measure of predictive accuracy for survival models. Motivated by a prostate cancer study, we address several issues associated with evaluating survival prediction models based on c-statistic with a focus on estimators using the technique of inverse probability of censoring weighting (IPCW). Compared to the existing work, we provide complete results on the asymptotic properties of the IPCW estimators under the assumption of coarsening at random (CAR), and propose a sensitivity analysis under the mechanism of noncoarseni

SUBMITTER: Wang M 

PROVIDER: S-EPMC4940324 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature