ArticleStatistics in medicine2020
The impact of covariance priors on arm-based Bayesian network meta-analyses with binary outcomes.
Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Bayesian hierarchical models incorporating study-level covariates for multivariate meta-analysis of diagnostic tests without a gold standard with application to COVID-19.Statistics in medicine · 2023Pooled it
- Two-stage Bayesian network meta-analysis of individualized treatment rules for multiple treatments with siloed data.Statistical methods in medical research · 2026Article
- 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
- Network meta-analysis made simple: A composite likelihood approach.Research synthesis methods · 2025Article
- Exploiting Multivariate Network Meta-Analysis: A Calibrated Bayesian Composite Likelihood Inference.Bayesian analysis · 2025Article
- Choice of Link Functions for Generalized Linear Mixed Models in Meta-Analyses of Proportions.Research methods in medicine & health sciences · 2025Article
- Tipping point analysis for the between-arm correlation in an arm-based evidence synthesis.BMC medical research methodology · 2024Article
- BRIDGING RANDOMIZED CONTROLLED TRIALS AND SINGLE-ARM TRIALS USING COMMENSURATE PRIORS IN ARM-BASED NETWORK META-ANALYSIS.The annals of applied statistics · 2021Article
- Bayesian meta-analysis using SAS PROC BGLIMM.Research synthesis methods · 2021Article
- Predictive P-score for treatment ranking in Bayesian network meta-analysis.BMC medical research methodology · 2021Article
- Article
- A variance shrinkage method improves arm-based Bayesian network meta-analysis.Statistical methods in medical research · 2021Article
- Application of network meta-analysis in the field of physical activity and health promotion.Journal of sport and health science · 2020Review
- A review and comparison of arm-based versus contrast-based network meta-analysis for binary outcomes-Understanding their differences and limitations.Wiley interdisciplinary reviews. Computational statisticsArticle
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4 authors.
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Abstract
Bayesian analyses with the arm-based (AB) network meta-analysis (NMA) model require researchers to specify a prior distribution for the covariance matrix of the treatment-specific event rates in a transformed scale, for example, the treatment-specific log-odds when a logit transformation is used. The commonly used conjugate prior for the covariance matrix, the inverse-Wishart (IW) distribution, has several limitations. For example, although the IW distribution is often described as noninformative or weakly informative, it may in fact provide strong information when some variance components are small (eg, when the standard deviation of study-specific log-odds of a treatment is smaller than 1/2), as is common in NMAs with binary outcomes. In addition, the IW prior generally leads to underestimation of correlations between treatment-specific log-odds, which are critical for borrowing strength across treatment arms to estimate treatment effects efficiently and to reduce potential bias. Alternatively, several separation strategies (ie, separate priors on variances and correlations) can be considered. To study the IW prior's impact on NMA results and compare it with separation strategies, we did simulation studies under different missing-treatment mechanisms. A separation strategy with appropriate priors for the correlation matrix and variances performs better than the IW prior, and should be recommended as the default vague prior in the AB NMA approach. Finally, we reanalyzed three case studies and illustrated the importance, when performing AB-NMA, of sensitivity analyses with different prior specifications on variances.
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