Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics
Abstract BackgroundData from multiple organizations are crucial for advancing learning health systems. However, ethical, legal, and social concerns may restrict the use of standard statistical methods that rely on pooling data. Although distributed algorithms offer alternative...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2024-11-01
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| Series: | JMIR Medical Informatics |
| Online Access: | https://medinform.jmir.org/2024/1/e53622 |
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| author | Félix Camirand Lemyre Simon Lévesque Marie-Pier Domingue Klaus Herrmann Jean-François Ethier |
| author_facet | Félix Camirand Lemyre Simon Lévesque Marie-Pier Domingue Klaus Herrmann Jean-François Ethier |
| author_sort | Félix Camirand Lemyre |
| collection | DOAJ |
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Abstract
BackgroundData from multiple organizations are crucial for advancing learning health systems. However, ethical, legal, and social concerns may restrict the use of standard statistical methods that rely on pooling data. Although distributed algorithms offer alternatives, they may not always be suitable for health frameworks.
ObjectiveThis study aims to support researchers and data custodians in three ways: (1) providing a concise overview of the literature on statistical inference methods for horizontally partitioned data, (2) describing the methods applicable to generalized linear models (GLMs) and assessing their underlying distributional assumptions, and (3) adapting existing methods to make them fully usable in health settings.
MethodsA scoping review methodology was used for the literature mapping, from which methods presenting a methodological framework for GLM analyses with horizontally partitioned data were identified and assessed from the perspective of applicability in health settings. Statistical theory was used to adapt methods and derive the properties of the resulting estimators.
ResultsFrom the review, 41 articles were selected and 6 approaches were extracted to conduct standard GLM-based statistical analysis. However, these approaches assumed evenly and identically distributed data across nodes. Consequently, statistical procedures were derived to accommodate uneven node sample sizes and heterogeneous data distributions across nodes. Workflows and detailed algorithms were developed to highlight information sharing requirements and operational complexity.
ConclusionsThis study contributes to the field of health analytics by providing an overview of the methods that can be used with horizontally partitioned data by adapting these methods to the context of heterogeneous health data and clarifying the workflows and quantities exchanged by the methods discussed. Further analysis of the confidentiality preserved by these methods is needed to fully understand the risk associated with the sharing of summary statistics. |
| format | Article |
| id | doaj-art-6df7c87a3c544485b6db280f6f86cb7b |
| institution | DOAJ |
| issn | 2291-9694 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Medical Informatics |
| spelling | doaj-art-6df7c87a3c544485b6db280f6f86cb7b2025-08-20T02:43:36ZengJMIR PublicationsJMIR Medical Informatics2291-96942024-11-0112e53622e5362210.2196/53622Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health AnalyticsFélix Camirand Lemyrehttp://orcid.org/0000-0003-3277-2729Simon Lévesquehttp://orcid.org/0009-0002-6994-2752Marie-Pier Dominguehttp://orcid.org/0009-0002-2582-6071Klaus Herrmannhttp://orcid.org/0000-0002-8044-5717Jean-François Ethierhttp://orcid.org/0000-0001-9408-0109 Abstract BackgroundData from multiple organizations are crucial for advancing learning health systems. However, ethical, legal, and social concerns may restrict the use of standard statistical methods that rely on pooling data. Although distributed algorithms offer alternatives, they may not always be suitable for health frameworks. ObjectiveThis study aims to support researchers and data custodians in three ways: (1) providing a concise overview of the literature on statistical inference methods for horizontally partitioned data, (2) describing the methods applicable to generalized linear models (GLMs) and assessing their underlying distributional assumptions, and (3) adapting existing methods to make them fully usable in health settings. MethodsA scoping review methodology was used for the literature mapping, from which methods presenting a methodological framework for GLM analyses with horizontally partitioned data were identified and assessed from the perspective of applicability in health settings. Statistical theory was used to adapt methods and derive the properties of the resulting estimators. ResultsFrom the review, 41 articles were selected and 6 approaches were extracted to conduct standard GLM-based statistical analysis. However, these approaches assumed evenly and identically distributed data across nodes. Consequently, statistical procedures were derived to accommodate uneven node sample sizes and heterogeneous data distributions across nodes. Workflows and detailed algorithms were developed to highlight information sharing requirements and operational complexity. ConclusionsThis study contributes to the field of health analytics by providing an overview of the methods that can be used with horizontally partitioned data by adapting these methods to the context of heterogeneous health data and clarifying the workflows and quantities exchanged by the methods discussed. Further analysis of the confidentiality preserved by these methods is needed to fully understand the risk associated with the sharing of summary statistics.https://medinform.jmir.org/2024/1/e53622 |
| spellingShingle | Félix Camirand Lemyre Simon Lévesque Marie-Pier Domingue Klaus Herrmann Jean-François Ethier Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics JMIR Medical Informatics |
| title | Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics |
| title_full | Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics |
| title_fullStr | Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics |
| title_full_unstemmed | Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics |
| title_short | Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics |
| title_sort | distributed statistical analyses a scoping review and examples of operational frameworks adapted to health analytics |
| url | https://medinform.jmir.org/2024/1/e53622 |
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