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DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models.

Chongliang Luo1,2, Md Nazmul Islam3, Natalie E Sheils3

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

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|March 31, 2022
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Summary
This summary is machine-generated.

We developed a privacy-preserving algorithm for distributed linear mixed models (DLMMs) that avoids sharing individual patient data. This method achieves lossless results, matching pooled data analyses for healthcare association studies.

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Area of Science:

  • Biostatistics
  • Health Informatics
  • Epidemiology

Background:

  • Linear mixed models (LMMs) are standard for multi-site healthcare data analysis.
  • Patient privacy regulations restrict sharing individual patient data (IPD) across sites.
  • Heterogeneous site-specific random effects pose analytical challenges.

Purpose of the Study:

  • To propose a distributed linear mixed model (DLMM) algorithm that enables multi-site analyses without IPD sharing.
  • To demonstrate the lossless property of the DLMM algorithm, ensuring identical results to pooled IPD analyses.
  • To apply the DLMM algorithm to a large-scale COVID-19 patient dataset.

Main Methods:

  • Developed a novel algorithm for fitting DLMMs requiring minimal aggregated data per site.
  • Implemented a single round of communication between sites.
  • Validated the algorithm's lossless property by comparing results with pooled IPD analysis.

Main Results:

  • The DLMM algorithm produced identical effect size and standard error estimates compared to pooled IPD.
  • The method successfully analyzed associations between patient characteristics and hospital stay duration in COVID-19 patients.
  • The study incorporated data from 120,609 COVID-19 patients across 11 international data sources.

Conclusions:

  • The proposed DLMM algorithm offers a privacy-preserving, efficient, and lossless solution for multi-site healthcare data analysis.
  • This approach facilitates robust association studies without compromising patient confidentiality.
  • The method is applicable to large-scale epidemiological research, including COVID-19 outcomes.