The Brier Score under Administrative Censoring: Problems and Solutions
This work solves a methodological problem for researchers and practitioners in survival analysis by providing a valid evaluation metric when censoring is administrative and covariate-dependent.
The paper addresses the problem of using the Brier score in survival analysis with right-censored data, showing that standard inverse probability of censoring weighting can be invalid when censoring times are identifiable from covariates, and proposes an alternative administrative Brier score that avoids this issue for administratively censored data.
The Brier score is commonly used for evaluating probability predictions. In survival analysis, with right-censored observations of the event times, this score can be weighted by the inverse probability of censoring (IPCW) to retain its original interpretation. It is common practice to estimate the censoring distribution with the Kaplan-Meier estimator, even though it assumes that the censoring distribution is independent of the covariates. This paper discusses the general impact of the censoring estimates on the Brier score and shows that the estimation of the censoring distribution can be problematic. In particular, when the censoring times can be identified from the covariates, the IPCW score is no longer valid. For administratively censored data, where the potential censoring times are known for all individuals, we propose an alternative version of the Brier score. This administrative Brier score does not require estimation of the censoring distribution and is valid even if the censoring times can be identified from the covariates.