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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Network meta-regression including baseline risk analysis and interactive visualizations as implemented by the
Tom Morris1, Janion Nevill1, Clareece Nevill1
1Biostatistics Research Group, Department of Population Health Sciences, University of Leicester, Leicester, UK; NIHR Complex Reviews Synthesis Unit, University of Leicester and University of Glasgow, Leicester, UK.
Journal of Clinical Epidemiology
|May 25, 2025
Summary
MetaInsight now offers network meta-regression (NMR) for single covariates, enabling researchers to explore heterogeneity and treatment interactions without statistical programming. This update enhances network meta-analysis (NMA) accessibility and reliability.
Area of Science:
- Biostatistics
- Health Informatics
- Clinical Epidemiology
Background:
- Network meta-analysis (NMA) is a statistical technique for comparing multiple treatments.
- Network meta-regression (NMR) extends NMA by incorporating study-level covariates to investigate heterogeneity and inconsistency.
- Existing tools often require advanced statistical programming skills for NMR.
Purpose of the Study:
- To describe the implementation and application of single-covariate network meta-regression (NMR) within the MetaInsight web application.
- To enable users to explore treatment-covariate interactions and heterogeneity in network meta-analyses (NMAs) without statistical programming.
- To correctly account for uncertainty when using baseline risk as a covariate.
Main Methods:
- NMR functionality was integrated into MetaInsight using the R packages gemtc and bnma.
- Users can select regression coefficient types: shared, exchangeable, or unrelated.
- New visualizations were developed to display covariate distributions and study contributions to comparisons.
Main Results:
- The paper details the new NMR features in MetaInsight.
- Illustrative examples and screenshots demonstrate the application of the functionality.
- The update facilitates complex meta-regression analyses through a user-friendly interface.
Conclusions:
- The enhanced MetaInsight application makes complex network meta-regression analyses accessible to a broader research community.
- This increased accessibility is expected to improve the quality and reliability of published NMAs.
- Improved NMA reporting can positively influence clinical decision-making.
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