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Learning from local to global: An efficient distributed algorithm for modeling time-to-event data.

Rui Duan1, Chongliang Luo1, Martijn J Schuemie2

  • 1Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Journal of the American Medical Informatics Association : JAMIA
|July 7, 2020
PubMed
Summary
This summary is machine-generated.

We developed a privacy-preserving algorithm for multicenter Cox proportional hazards modeling without sharing patient data. This One-shot Distributed Algorithm (ODAC) accurately estimates rare event risks across sites, outperforming traditional meta-analysis.

Keywords:
Cox proportional hazards modeldata integrationdistributed algorithmelectronic health recordmeta-analysis

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Area of Science:

  • Biostatistics
  • Health Informatics
  • Epidemiology

Background:

  • Multicenter studies are crucial for robust statistical analysis, but sharing patient-level data raises privacy concerns.
  • Traditional methods like meta-analysis can introduce bias, especially with rare events.

Purpose of the Study:

  • To develop and evaluate a privacy-preserving distributed algorithm for multicenter Cox proportional hazards modeling.
  • To assess the performance of the One-shot Distributed Algorithm (ODAC) compared to pooled and meta-analysis estimators.

Main Methods:

  • Developed a novel One-shot Distributed Algorithm (ODAC) that uses site-specific aggregated data to approximate a global Cox model.
  • Validated ODAC through simulation studies and a real-world application using Observational Health Data Sciences and Informatics (OHDSI) network data.

Main Results:

  • ODAC provided estimates with minimal relative bias (<0.1%) in simulations, comparable to a pooled estimator using all patient data.
  • ODAC significantly outperformed meta-analysis, especially for rare events (low event rates), where meta-analysis showed substantial bias.
  • In the OHDSI network, ODAC demonstrated low bias for most hazard ratio estimates, unlike meta-analysis.

Conclusions:

  • ODAC is an effective, noniterative, privacy-preserving method for distributed time-to-event analyses across multiple institutions.
  • The algorithm is highly suitable for studying rare events and diseases due to its accuracy and outperformance of meta-analysis in such scenarios.