Integrating high-dimensional censored data under privacy constraints via localized computations

Limited sample size and censoring inherently limit the statistical efficiency of high-dimensional data analysis. While integrating data from multiple sources can enhance estimation efficiency, concerns remain regarding data privacy breaches and between-site heterogeneity. In this paper, we propose a privacy-preserving approach to integrate the high-dimensional right-censored data with source-level heterogeneity. The proposed method is based on the local computation strategy: each site can obtain an integrative estimation based on its local full dataset and the summary statistics from other sites. For each party, this strategy not only meets the data privacy constraints but also maximizes its local data's utilization. Moreover, we introduce a refined procedure for practical use to avoid the shrinkage of the local covariate effect that is unique across all sites. Theoretical results of the proposed estimates including consistency, asymptotic normality and efficiency gains are attained. Simulation experiments demonstrate its superiority over the integrative methods relying solely on summary statistics and the local estimations. The application to multi-source clinical data of ovarian cancer further verifies its practical effectiveness.