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Updated: Jul 5, 2025
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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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One-shot distributed algorithms for addressing heterogeneity in competing risks data across clinical sites
Dazheng Zhang1, Jiayi Tong2, Ronen Stein3
1The Center for Health Analytics and Synthesis of Evidence (CHASE), University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA. Electronic address: https://twitter.com/DazhengZ.
Journal of Biomedical Informatics
|January 20, 2024
Summary
We developed a novel algorithm to analyze pediatric Crohn's disease data across multiple hospitals, identifying key risk factors for disease behavior. This approach effectively integrates distributed data while accounting for patient variations.
Area of Science:
- Biostatistics
- Pediatric Gastroenterology
- Health Informatics
Background:
- Analyzing multi-site pediatric data presents challenges due to patient heterogeneity.
- Understanding risk factors for pediatric Crohn's disease (PCD) behaviors like penetrating or stricturing is crucial for effective treatment.
Approach:
- Introduced the ODACoRH algorithm, a one-shot distributed method for competing risks with heterogeneity.
- Developed a surrogate likelihood function combining local and aggregated external data.
- Validated ODACoRH through simulations and replication of the RISK study in a pediatric cohort.
Key Points:
- ODACoRH demonstrated minimal relative bias (-0.2%) compared to meta-analysis (-11.4%).
- Results from ODACoRH align with pooled data, confirming the reliability of federated learning.
- Identified clinical risk factors for PCD consistent with existing literature.
Conclusions:
- The ODACoRH algorithm effectively integrates decentralized data across sites, managing heterogeneity.
- This study highlights significant clinical risk factors for PCD requiring further investigation.
Keywords:
Communication-efficientCompeting risks modelDistributed research networkFederated learningOne-shot distributed algorithmMore Related Videos
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