Distributed optimal subsampling inference for the additive hazards model with massive survival data

Analyzing massive survival data from distributed sources, such as multicenter registries, is hindered by computational bottlenecks and privacy constraints that preclude centralized pooling. This paper addresses these obstacles for the additive hazards model by proposing a distributed inference framework that integrates optimal subsampling with divide-and-conquer aggregation. Each site draws a local subsample and transmits only low-dimensional summary statistics to a central server, which constructs a global estimator. Consistency and asymptotic normality of the resulting estimator are established under some mild regularity conditions. Extensive simulation studies show stable finite-sample performance across a range of covariate distributions, censoring rates, and levels of heterogeneity between sites. Finally, the practical utility of our methodology is illustrated through an application demonstrating the computational gains of the proposed method.