ArticleStatistics in medicine2019
A comparison of arm-based and contrast-based models for network meta-analysis.
Article in Statistics in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 5 of them syntheses that pooled it.
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Who cites it
45 citing papers in PubMed, 5 syntheses or guidelines pooled it.
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- Performance of several types of beta-binomial models in comparison to standard approaches for meta-analyses with very few studies.BMC medical research methodology · 2022Pooled it
- Bayesian Methods for Meta-Analyses of Binary Outcomes: Implementations, Examples, and Impact of Priors.International journal of environmental research and public health · 2021Pooled it
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- Trials and triangles: Network meta-analysis of multi-arm trials with correlated arms.Research synthesis methods · 2025Article
- Tipping point analysis in network meta-analysis.Research synthesis methods · 2025Article
- One-step parametric network meta-analysis models using the exact likelihood that allow for time-varying treatment effects.Research synthesis methods · 2025Article
- An investigation of the impact of using contrast- and arm-synthesis models for network meta-analysis.Research synthesis methods · 2025Article
- Risk of Bias in Network Meta-Analysis (RoB NMA) tool.BMJ (Clinical research ed.) · 2025Article
- Network meta-analysis made simple: A composite likelihood approach.Research synthesis methods · 2025Article
- Network Meta-Analysis With Individual Participant-Level Data of Time-to-Event Outcomes Using Cox Regression.Statistics in medicine · 2025Article
- Comparative Effectiveness and Safety of the JAK Inhibitors and Biologic Disease-Modifying Antirheumatic Drugs in Treating Children With Nonsystemic Juvenile Idiopathic Arthritis: A Bayesian Meta-Analysis of Randomized Controlled Trials.ACR open rheumatology · 2025Article
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4 authors.
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Abstract
Differences between arm-based (AB) and contrast-based (CB) models for network meta-analysis (NMA) are controversial. We compare the CB model of Lu and Ades (2006), the AB model of Hong et al(2016), and two intermediate models, using hypothetical data and a selected real data set. Differences between models arise primarily from study intercepts being fixed effects in the Lu-Ades model but random effects in the Hong model, and we identify four key difference. (1) If study intercepts are fixed effects then only within-study information is used, but if they are random effects then between-study information is also used and can cause important bias. (2) Models with random study intercepts are suitable for deriving a wider range of estimands, eg, the marginal risk difference, when underlying risk is derived from the NMA data; but underlying risk is usually best derived from external data, and then models with fixed intercepts are equally good. (3) The Hong model allows treatment effects to be related to study intercepts, but the Lu-Ades model does not. (4) The Hong model is valid under a more relaxed missing data assumption, that arms (rather than contrasts) are missing at random, but this does not appear to reduce bias. We also describe an AB model with fixed study intercepts and a CB model with random study intercepts. We conclude that both AB and CB models are suitable for the analysis of NMA data, but using random study intercepts requires a strong rationale such as relating treatment effects to study intercepts.
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