With recent advancement in cancer screening and treatment, many patients with cancers are identified at early stage and clinically cured. Importantly, uncured patients should be treated timely before the cancer progresses to advanced stages for which therapeutic options are rather limited. It is also crucial to identify uncured subjects among patients with early-stage cancers for clinical trials to develop effective adjuvant therapies. Thus, it is of interest to develop statistical predictive models with as high accuracy as possible in predicting the latent cure status. The receiver operating characteristic curve (ROC) and the area under the ROC curve (AUC) are among the most widely used statistical metrics for assessing predictive accuracy or discriminatory power for a dichotomous outcome (cured/uncured). Yet the conventional AUC cannot be directly used due to incompletely observed cure status. In this article, we proposed new estimates of the ROC curve and its AUC for predicting latent cure status in Cox proportional hazards (PH) cure models and transformation cure models. We developed explicit formulas to estimate sensitivity, specificity, the ROC and its AUC without requiring to know the patient cure status. We also developed EM type estimates to approximate sensitivity, specificity, ROC and AUC conditional on observed data. Numerical studies were used to assess their finite-sample performance of the proposed methods. Both methods are consistent and have similar efficiency as shown in our numerical studies. A melanoma dataset was used to demonstrate the utility of the proposed estimates of the ROC curve for the latent cure status. We also have developed an [Formula: see text] package called [Formula: see text] to efficiently compute the proposed estimates.
Keywords: Area under the ROC curve; Latent cure status; Mixture cure models; Predictive accuracy; Sensitivity.