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Fengling Hu1, Jiayi Tong2,3, Margaret Gardner4

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|January 8, 2026
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We developed distributed Generalized Additive Models for Location, Scale, and Shape (GAMLSS) to create population reference charts from multi-site data without sharing patient information. This enables accurate, privacy-preserving analysis across diverse clinical settings.

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Area of Science:

  • Biostatistics
  • Computational Biology
  • Data Science

Background:

  • Estimating population reference ranges across age and sex is crucial for identifying atypical measurements.
  • Generalized Additive Models for Location, Scale, and Shape (GAMLSS) are recommended for modeling non-linear growth and population heterogeneity.
  • Challenges in data sharing for GAMLSS model fitting due to privacy and practical constraints hinder multi-site studies.

Purpose of the Study:

  • To introduce a privacy-preserving distributed algorithm for fitting GAMLSS models across multiple sites.
  • To enable the construction of population reference charts without sharing patient-level data.
  • To address the need for federated learning algorithms applicable to GAMLSS.

Main Methods:

  • Proposed distributed GAMLSS (dGAMLSS), a novel distributed algorithm for fitting GAMLSS models in a federated manner.
  • Incorporated specific strategies for fitting smooth functions with varying communication efficiency.
  • Developed an R package (dGAMLSS) for implementing the algorithm and managing site-specific parameters.

Main Results:

  • Demonstrated the effectiveness of dGAMLSS in constructing population reference charts across clinical, genomics, and neuroimaging data.
  • Showcased the ability of dGAMLSS to accurately reproduce pooled reference charts and statistical inference.
  • Validated the algorithm's performance in scenarios requiring privacy preservation.

Conclusions:

  • dGAMLSS successfully enables the creation of population reference charts using multi-site data while preserving patient privacy.
  • The algorithm facilitates robust statistical modeling and inference in distributed settings where data cannot be pooled.
  • dGAMLSS offers a practical solution for collaborative research requiring sensitive health data.