🤖 AI Summary
This study addresses regression modeling for recurrent events in the presence of competing risks when both administrative and random right censoring coexist. The authors propose a unified unbiased estimation strategy that integrates risk-set adjustments with inverse probability of censoring weighting (IPCW) to separately account for the two distinct censoring mechanisms, thereby avoiding excessive modeling assumptions about the censoring process. For the first time, this approach systematically combines administrative and random censoring to yield consistent estimators for parameters in both Ghosh–Lin and Fine–Gray models. Under minimal modeling assumptions, the method substantially enhances estimation robustness and applicability, making it well-suited for large-scale registry databases and clinical trial data.
📝 Abstract
Recurrent events or competing risks regression models are often applied in the bio-medical setting and both can be considered as marginal models. In presence of right-censoring, such models need to be adjusted to give consistent estimators. When censoring is administrative, marginal regression models are particularly easy to estimate. However, when censoring is instead acting randomly, inverse probability of censoring weighting (IPCW) adjustments are typically considered to obtain parameter estimates. This technique relies on a censoring-weights adjustment via a correct censoring model, but for administrative censoring the adjustment is done correctly simply by modifying the risk-set. In practice for large central registries or some clinical trials, the administrative censoring time will be known for all subjects, but there will typically also be a proportion of subjects that are censored at random. In this work, we consider two frequently used regression approaches, the Ghosh-Lin model for recurrent events with terminal events and the Fine-Gray model for competing events. For these two settings, when both administrative and random censoring are present, we demonstrate how to obtain correct estimation by dealing with the combination of the two different types of censoring relying on a minimum of modeling assumptions.