‘Arm‐based’ parameterization for network meta‐analysis
Open accessAbstract
We present an alternative to the contrast‐based parameterization used in a number of publications for network meta‐analysis. This alternative “arm‐based” parameterization offers a number of advantages: it allows for a “long” normalized data structure that remains constant regardless of the number of comparators; it can be used to directly incorporate individual patient data into the analysis; the incorporation of multi‐arm trials is straightforward and avoids the need to generate a multivariate distribution describing treatment effects; there is a direct mapping between the parameterization and the analysis script in languages such as WinBUGS and finally, the arm‐based parameterization allows simple extension to treatment‐specific random treatment effect variances. We validated the parameterization using a published smoking cessation dataset. Network meta‐analysis using arm‐ and contrast‐based parameterizations produced comparable results (with means and standard deviations being within +/− 0.01) for both fixed and random effects models. We recommend that analysts consider using arm‐based parameterization when carrying out network meta‐analyses. © 2015 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd.
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Frequentist arm-based network meta-analysis permits multiple treatment comparisons, including indirect comparisons of treatments not directly compared with a control treatment, using arm-level data.
“This approach permits multiple treatment comparisons, even indirect comparisons of treatments that have not been directly compared with a control treatment (e.g., uncontrolled studies) using arm-level data (rather than between-group differences) (21).”
From Global diabetes prevention interventions: A systematic review and network meta-analysis of the real-world impact on incidence, weight, and glucose (2018) · cites this paper as [21]
The Introduction says the model uses one row per trial arm and was tested in networks where some treatments were not directly compared with the reference treatment. It also explicitly says the paper implements the model in a Bayesian framework, not a frequentist one. The Discussion’s example uses two comparative trials, so it does not establish the claim’s suggestion that uncontrolled studies can be included. (LLM, conf=0.89)
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Cites
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Galaviz, K. I., Weber, M. B., Straus, A., Haw, J. S., Narayan, K. M. V., & Ali, M. K. (2018) · Diabetes Care · doi:10.2337/dc17-2222 · Cited by 289 · see citing works
Network meta‐analysis of multicomponent interventions
Rücker, G., Petropoulou, M., & Schwarzer, G. (2020) · Biometrical Journal · doi:10.1002/bimj.201800167 · Cited by 195 · see citing works
Interventions for Reversing Prediabetes: A Systematic Review and Meta-Analysis
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- Authors
- Hawkins, N., Scott, D. A., & Woods, B.
- Affiliations
- ICON Health Economics Oxford OX2 0JJ UK
- University of York (GB)
- Corresponding author
- Woods, Beth
- Venue
- Research Synthesis Methods
- Publisher
- Wiley
- Published
- 2015-11-27
- Volume
- 7
- Issue
- 3
- Pages
- 306-313
- ISSN
- 1759-2879 (ISSN-L)
- 1759-2887
- PMC
- PMC5063191
- OpenAlex
- W2183872742
- Cited by
- 35
- References
- 3
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 3 · 2023: 11 · 2022: 2 · 2020: 5 · 2019: 3 · 2018: 4 · 2017: 1 · 2015: 1
- FWCI
- 1.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Research Synthesis Methods (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion)
- ePrints Soton (University of Southampton) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- network meta-analysis, multi-arm trials, individual patient data, treatment effect heterogeneity, smoking cessation, WinBUGS, Bayesian meta-analysis
- MeSH terms
- Network Meta-Analysis, Network Meta-Analysis as Topic, Algorithms, Clinical Trials as Topic, Data Interpretation, Statistical, Humans, Programming Languages, Research Design, Risk, Software, Tobacco Use Disorder, Multivariate Analysis, Randomized Controlled Trials as Topic, Smoking Cessation
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