CEDAR: Communication Efficient Distributed Analysis for Regressions

TL;DR

This paper introduces CEDAR, a communication-efficient distributed method for regression analysis on EHR data that preserves privacy, improves efficiency, and enables statistical inference without sharing raw patient data.

Contribution

The paper proposes a novel distributed regression method that incorporates posterior samples for enhanced efficiency and privacy, with theoretical guarantees and practical validation.

Findings

Achieves accurate regression estimates without sharing raw data

Maintains differential privacy while improving efficiency

Performs well in simulations and real EHR data analyses

Abstract

Electronic health records (EHRs) offer great promises for advancing precision medicine and, at the same time, present significant analytical challenges. Particularly, it is often the case that patient-level data in EHRs cannot be shared across institutions (data sources) due to government regulations and/or institutional policies. As a result, there are growing interests about distributed learning over multiple EHRs databases without sharing patient-level data. To tackle such challenges, we propose a novel communication efficient method that aggregates the local optimal estimates, by turning the problem into a missing data problem. In addition, we propose incorporating posterior samples of remote sites, which can provide partial information on the missing quantities and improve efficiency of parameter estimates while having the differential privacy property and thus reducing the risk of information leaking. The proposed approach, without sharing the raw patient level data, allows for proper statistical inference and can accommodate sparse regressions. We provide theoretical investigation for the asymptotic properties of the proposed method for statistical inference as well as differential privacy, and evaluate its performance in simulations and real data analyses in comparison with several recently developed methods.