ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2020
Robust-ODAL: Learning from heterogeneous health systems without sharing patient-level data.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Learning competing risks across multiple hospitals: one-shot distributed algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Pooled it
- One-shot distributed algorithms for addressing heterogeneity in competing risks data across clinical sites.Journal of biomedical informatics · 2024Pooled it
- Distributed Quasi-Poisson regression algorithm for modeling multi-site count outcomes in distributed data networks.Journal of biomedical informatics · 2022Pooled it
- ODACH: a one-shot distributed algorithm for Cox model with heterogeneous multi-center data.Scientific reports · 2022Pooled it
- dGAMLSS: an exact, distributed algorithm to fit Generalized Additive Models for Location, Scale, and Shape for privacy-preserving population reference charts.Bioinformatics (Oxford, England) · 2026Article
- Beyond REMS & PDMPs: A Proposed Framework for Next-Generation Opioid Regulation.Therapeutic innovation & regulatory science · 2026Review
- Federated analytics for non-communicable disease surveillance in the European health data space: a scoping review and conceptual framework.Frontiers in public health · 2026Article
- Advancing Interpretable Regression Analysis for Binary Data: A Novel Distributed Algorithm Approach.Statistics in medicine · 2024Article
- Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics.JMIR medical informatics · 2024Article
- Centralized and Federated Models for the Analysis of Clinical Data.Annual review of biomedical data science · 2024Review
- Federated and distributed learning applications for electronic health records and structured medical data: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Multisite learning of high-dimensional heterogeneous data with applications to opioid use disorder study of 15,000 patients across 5 clinical sites.Scientific reports · 2022Article
- Distributed learning for heterogeneous clinical data with application to integrating COVID-19 data across 230 sites.NPJ digital medicine · 2022Article
- Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints.Journal of machine learning research : JMLR · 2021Article
- Accelerating ophthalmic artificial intelligence research: the role of an open access data repository.Current opinion in ophthalmology · 2020Review
- Review of Clinical Research Informatics.Yearbook of medical informatics · 2020Review
- Identifying Clinical Risk Factors for Opioid Use Disorder using a Distributed Algorithm to Combine Real-World Data from a Large Clinical Data Research Network.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Leverage Real-world Longitudinal Data in Large Clinical Research Networks for Alzheimer's Disease and Related Dementia (ADRD).AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Electronic Health Records (EHR) contain extensive patient data on various health outcomes and risk predictors, providing an efficient and wide-reaching source for health research. Integrated EHR data can provide a larger sample size of the population to improve estimation and prediction accuracy. To overcome the obstacle of sharing patient-level data, distributed algorithms were developed to conduct statistical analyses across multiple clinical sites through sharing only aggregated information. However, the heterogeneity of data across sites is often ignored by existing distributed algorithms, which leads to substantial bias when studying the association between the outcomes and exposures. In this study, we propose a privacy-preserving and communication-efficient distributed algorithm which accounts for the heterogeneity caused by a small number of the clinical sites. We evaluated our algorithm through a systematic simulation study motivated by real-world scenarios and applied our algorithm to multiple claims datasets from the Observational Health Data Sciences and Informatics (OHDSI) network. The results showed that the proposed method performed better than the existing distributed algorithm ODAL and a meta-analysis method.
Indexed as
Identifiers
31797639PMC6905508What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.