AbstractTime‐dependent receiver operating characteristic curves are often used to evaluate the classification performance of continuous measures when considering time‐to‐event data. When one is interested in evaluating the predictive performance of multiple covariates, it is common to use the Cox proportional hazards model to obtain risk scores; however, previous work has shown that when the model is mis‐specified, the estimand corresponding to the partial likelihood estimator depends on the censoring distribution. In this manuscript, we show that when the risk score model is mis‐specified, the AUC will also depend on the censoring distribution, leading to either over‐ or under‐estimation of the risk score's predictive performance. We propose the use of censoring‐robust estimators to remove the dependence on the censoring distribution and provide empirical results supporting the use of censoring‐robust risk scores.