Distributed additive hazards regression analysis of multi-site current status data without using individual-level data

In multi-site studies, sharing individual-level information across multiple data-contributing sites usually poses a significant risk to data security. Thus, due to privacy constraints, analytical tools without using individual-level data have drawn considerable attention to researchers in recent years. In this work, we consider regression analysis of current status data arising from multi-site cross-sectional studies and develop two distributed estimation methods tailored to the unstratified and stratified additive hazards models, respectively. In particular, instead of utilizing the individual-level data, the proposed methods only require transferring the summary statistics from each site to the analysis center, which achieves the aim of privacy protection. We establish the asymptotic properties of the proposed estimators, including the consistency and asymptotic normality. Specifically, the distributed estimators derived are shown to be asymptotically equivalent to those based on the pooled individual-level data. Simulation studies and an application to a multi-site gonorrhea infection data set demonstrate the proposed methods' satisfactory performance and practical utility.