Time-Dependent Predictive Accuracy Metrics in the Context of Interval Censoring and Competing Risks.

ABSTRACT: Evaluating the performance of a prediction model is a common task in medical statistics. Standard accuracy metrics require the observation of the true outcomes. This is typically not possible in the setting with time-to-event outcomes due to censoring. Interval censoring, the presence of time-varying covariates, and competing risks present additional challenges in obtaining those accuracy metrics. In this study, we propose two methods to deal with interval censoring in a time-varying competing risk setting: a model-based approach and the inverse probability of censoring weighting (IPCW) approach, focusing on three key time-dependent metrics: area under the receiver-operating characteristic curve, Brier score, and expected predictive cross-entropy. The evaluation is conducted over a medical

SUBMITTER: Yang Z 

PROVIDER: S-EPMC12766878 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature