Faculty, Staff and Student Publications
Publication Date
2-1-2022
Journal
IEEE Transactions on Knowledge and Data Engineering
Abstract
The Cox proportional hazards model is a popular semi-parametric model for survival analysis. In this paper, we aim at developing a federated algorithm for the Cox proportional hazards model over vertically partitioned data (i.e., data from the same patient are stored at different institutions). We propose a novel algorithm, namely VERTICOX, to obtain the global model parameters in a distributed fashion based on the Alternating Direction Method of Multipliers (ADMM) framework. The proposed model computes intermediary statistics and exchanges them to calculate the global model without collecting individual patient-level data. We demonstrate that our algorithm achieves equivalent accuracy for the estimation of model parameters and statistics to that of its centralized realization. The proposed algorithm converges linearly under the ADMM framework. Its computational complexity and communication costs are polynomially and linearly associated with the number of subjects, respectively. Experimental results show that VERTICOX can achieve accurate model parameter estimation to support federated survival analysis over vertically distributed data by saving bandwidth and avoiding exchange of information about individual patients. The source code for VERTICOX is available at: https://github.com/daiwenrui/VERTICOX.
Keywords
Federated survival analysis, Cox proportional hazards model, alternating direction method of multipliers, vertically partitioned data, privacy protection
DOI
10.1109/tkde.2020.2989301
PMID
36158636
PMCID
PMC9491599
PubMedCentral® Posted Date
2-1-2023
PubMedCentral® Full Text Version
Author MSS
Published Open-Access
yes
