ArticleResearch synthesis methods2016
A Bayesian missing data framework for generalized multiple outcome mixed treatment comparisons.
Article in Research synthesis methods, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 9 of them syntheses that pooled it.
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Who cites it
64 citing papers in PubMed, 9 syntheses or guidelines pooled it.
- Pooled it
- Accounting for post-randomization variables in meta-analysis: A joint meta-regression approach.Biometrics · 2023Pooled it
- A penalization approach to random-effects meta-analysis.Statistics in medicine · 2022Pooled it
- Meta-analysis of rare adverse events in randomized clinical trials: Bayesian and frequentist methods.Clinical trials (London, England) · 2021Pooled it
- Efficacy and safety of frontline regimens for older transplant-ineligible patients with multiple myeloma: A systematic review and meta-analysis.Journal of geriatric oncology · 2020Pooled it
- A Bayesian multivariate meta-analysis of prevalence data.Statistics in medicine · 2020Pooled it
- A network meta-analysis of interproximal oral hygiene methods in the reduction of clinical indices of inflammation.Journal of periodontology · 2018Pooled it
- Comparative Effectiveness of Published Interventions for Elderly Fall Prevention: A Systematic Review and Network Meta-Analysis.International journal of environmental research and public health · 2018Pooled it
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- Bayesian Network Meta-Analysis With One or Two Continuous Outcomes Measured at Multiple Time Points Using Gaussian Random Walks With Drift.Statistics in medicine · 2026Article
- Resting energy expenditure of women with and without polycystic ovary syndrome: a systematic review and meta-analysis.medRxiv : the preprint server for health sciences · 2026Article
- Lord's Paradox and two network meta-analysis models.Research synthesis methods · 2026Article
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- 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
- Exploiting Multivariate Network Meta-Analysis: A Calibrated Bayesian Composite Likelihood Inference.Bayesian analysis · 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
4 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
Abstract
Bayesian statistical approaches to mixed treatment comparisons (MTCs) are becoming more popular because of their flexibility and interpretability. Many randomized clinical trials report multiple outcomes with possible inherent correlations. Moreover, MTC data are typically sparse (although richer than standard meta-analysis, comparing only two treatments), and researchers often choose study arms based upon which treatments emerge as superior in previous trials. In this paper, we summarize existing hierarchical Bayesian methods for MTCs with a single outcome and introduce novel Bayesian approaches for multiple outcomes simultaneously, rather than in separate MTC analyses. We do this by incorporating partially observed data and its correlation structure between outcomes through contrast-based and arm-based parameterizations that consider any unobserved treatment arms as missing data to be imputed. We also extend the model to apply to all types of generalized linear model outcomes, such as count or continuous responses. We offer a simulation study under various missingness mechanisms (e.g., missing completely at random, missing at random, and missing not at random) providing evidence that our models outperform existing models in terms of bias, mean squared error, and coverage probability then illustrate our methods with a real MTC dataset. We close with a discussion of our results, several contentious issues in MTC analysis, and a few avenues for future methodological development.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.