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  3. Distributed Statistical Analyses: A Scoping Review And Examples Of Operational Frameworks Adapted To Health Analytics.
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  3. Distributed Statistical Analyses: A Scoping Review And Examples Of Operational Frameworks Adapted To Health Analytics.

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Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health

Félix Camirand Lemyre1,2, Simon Lévesque1,2,3, Marie-Pier Domingue1,2,4

  • 1GRIIS, Université de Sherbrooke, 2500, Boul de l'Université, Sherbrooke, QC, J1K 2R1, Canada, 1-819-821-8000 ext 74977.

JMIR Medical Informatics
|January 8, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study adapts statistical inference methods for distributed health data, addressing ethical concerns and data heterogeneity. New procedures accommodate varying sample sizes and distributions for generalized linear models in health analytics.

Keywords:
GLMsalgorithmsdata custodiansdata sciencedistributed algorithmsdistributed analysisfederated analysisgeneralized linear modelshorizontally partitioned datalearning health systemsreview methodsscopingsearchesstatisticssynthesis

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Area of Science:

  • Health Analytics
  • Statistical Inference
  • Distributed Data Systems

Background:

  • Advancing learning health systems requires multi-organizational data, but ethical and legal issues impede standard data pooling.
  • Distributed algorithms offer alternatives but may not fit all health frameworks.
  • Heterogeneous data and privacy concerns necessitate specialized statistical approaches.

Purpose of the Study:

  • To provide a literature overview of statistical inference for horizontally partitioned data.
  • To describe methods for generalized linear models (GLMs) and their assumptions in health settings.
  • To adapt existing methods for practical use with heterogeneous health data.

Main Methods:

  • A scoping review identified 6 approaches for GLM analysis with horizontally partitioned data.
  • Statistical theory was applied to adapt methods for health data.
  • Workflows and algorithms were developed to detail information sharing and complexity.

    • Main Results:

    • Six statistical analysis approaches for GLMs with horizontally partitioned data were identified.
    • Methods were adapted to handle uneven sample sizes and heterogeneous data distributions across nodes.
    • Detailed algorithms and workflows were created to illustrate operational aspects.

    Conclusions:

    • This work offers adapted statistical methods for analyzing heterogeneous, horizontally partitioned health data.
    • The study clarifies workflows and data exchange needs for distributed health analytics.
    • Further research is needed on the confidentiality of shared summary statistics.