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Published on: July 3, 2020
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[Progress in method development and application of distributed learning for estimation of epidemiological effect].
1Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China.
Summary
Distributed learning shows promise for estimating epidemiological effects, offering advantages over meta-analysis, especially with data heterogeneity and rare outcomes. Further research is needed to improve its efficiency and handle diverse data structures.
Area of Science:
- Epidemiology
- Health Big Data
- Machine Learning
Background:
- Distributed learning (DL) is emerging in health big data research.
- Traditional methods face challenges with multi-center data privacy and heterogeneity.
Purpose of the Study:
- To systematically review distributed learning methods for epidemiological effect estimation.
- To provide methodological guidance for multi-center studies using DL.
Main Methods:
- Literature search of "health/medical big data" and "distributed/federated learning" up to December 2023.
- Inclusion/exclusion criteria and data extraction framework developed with expert consultation.
- Screening and data extraction performed by two independent researchers.
Main Results:
- 29 papers published mainly from the US since 2019 were analyzed.
- 22 DL methods developed for logistic, Cox, Poisson regression, and GLMM, plus 3 analysis platforms.
- DL methods showed low bias in 1-3 communication rounds, outperforming meta-analysis in handling data heterogeneity and rare outcomes.
Conclusions:
- Distributed learning is a promising approach for epidemiological effect estimation.
- Further research is required to address data heterogeneity and improve communication efficiency.
- DL methods need further development for robust application in multi-center studies.
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