ArticleStatistical methods in medical research2019
Bayesian hierarchical methods for meta-analysis combining randomized-controlled and single-arm studies.
Article in Statistical methods in medical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prevalence of Healed Plaque and Factors Influencing Its Characteristics Under Optical Coherence Tomography in Patients With Coronary Artery Disease: A Systematic Review, Meta-Analysis, and Meta-Regression.Frontiers in cardiovascular medicine · 2021Pooled it
- A Systematic Risk Assessment and Meta-Analysis on the Use of Oral β-Alanine Supplementation.Advances in nutrition (Bethesda, Md.) · 2019Pooled it
- Combining external placebo-arm data in a prevention trial: effects of cocoa extract supplementation on cardiovascular disease.European journal of epidemiology · 2026Article
- Tipping point analysis for the between-arm correlation in an arm-based evidence synthesis.BMC medical research methodology · 2024Article
- Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group.BMC medical research methodology · 2023Article
- Bayesian hierarchical model-based network meta-analysis to overcome survival extrapolation challenges caused by data immaturity.Journal of comparative effectiveness research · 2023Article
- Bayesian network meta-analysis methods for combining individual participant data and aggregate data from single arm trials and randomised controlled trials.BMC medical research methodology · 2022Article
- BRIDGING RANDOMIZED CONTROLLED TRIALS AND SINGLE-ARM TRIALS USING COMMENSURATE PRIORS IN ARM-BASED NETWORK META-ANALYSIS.The annals of applied statistics · 2021Article
- Incorporating single-arm studies in meta-analysis of randomised controlled trials: a simulation study.BMC medical research methodology · 2021Article
- 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
- Data Integration in Causal Inference.Wiley interdisciplinary reviews. Computational statisticsArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Meta-analysis of interventions usually relies on randomized controlled trials. However, when the dominant source of information comes from single-arm studies, or when the results from randomized controlled trials lack generalization due to strict inclusion and exclusion criteria, it is vital to synthesize both sources of evidence. One challenge of synthesizing both sources is that single-arm studies are usually less reliable than randomized controlled trials due to selection bias and confounding factors. In this paper, we propose a Bayesian hierarchical framework for the purpose of bias reduction and efficiency gain. Under this framework, three methods are proposed: bivariate generalized linear mixed effects models, hierarchical power prior model and hierarchical commensurate prior model. Design difference and potential biases are considered in all models, within which the hierarchical power prior and hierarchical commensurate prior models further offer to downweight single-arm studies flexibly. The hierarchical commensurate prior model is recommended as the primary method for evidence synthesis because of its accuracy and robustness. We illustrate our methods by applying all models to two motivating datasets and evaluate their performance through simulation studies. We finish with a discussion of the advantages and limitations of our methods, as well as directions for future research in this area.
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