ArticleBMC medical research methodology2018
Hypothesis testing in Bayesian network meta-analysis.
Article in BMC medical research methodology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 5 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Pooled it
- Efficacy and safety of multiple external therapies in patients with insomnia: a systematic review and network meta-analysis.Frontiers in neurology · 2024Pooled it
- Effects of externally-applied, non-pharmacological Interventions on short- and long-term symptoms and inflammatory cytokine levels in patients with knee osteoarthritis: a systematic review and network meta-analysis.Frontiers in immunology · 2023Pooled it
- Comparison of the transanal surgical techniques for local excision of rectal tumors: a network meta-analysis.International journal of colorectal disease · 2020Pooled it
- Role of Systemic Treatment for Advanced/Metastatic Gastric Carcinoma in the Third-Line Setting: A Bayesian Network Analysis.Frontiers in oncology · 2020Pooled it
- Ranking of treatments in network meta-analysis: incorporating minimally important differences.BMC medical research methodology · 2025Article
- Sequential versus concomitant treatment of androgen receptor signaling inhibitors and docetaxel for metastatic hormone-sensitive prostate cancer: an network meta-analysis.Frontiers in pharmacology · 2024Article
- A guide to appropriately planning and conducting meta-analyses: part 3. Special considerations-the network meta-analysis.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2023Review
- Interpreting and assessing confidence in network meta-analysis results: an introduction for clinicians.Journal of anesthesia · 2022Review
- A Systematic Literature Review and Indirect Treatment Comparison of Efficacy of Repository Corticotropin Injection versus Synthetic Adrenocorticotropic Hormone for Infantile Spasms.Journal of health economics and outcomes research · 2021Article
- Role of immune checkpoint inhibitor-based therapies for metastatic renal cell carcinoma in the first-line setting: A Bayesian network analysis.EBioMedicine · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNetwork meta-analysis is an extension of the classical pairwise meta-analysis and allows to compare multiple interventions based on both head-to-head comparisons within trials and indirect comparisons across trials. Bayesian or frequentist models are applied to obtain effect estimates with credible or confidence intervals. Furthermore, p-values or similar measures may be helpful for the comparison of the included arms but related methods are not yet addressed in the literature. In this article, we discuss how hypothesis testing can be done in a Bayesian network meta-analysis.
methodsAn index is presented and discussed in a Bayesian modeling framework. Simulation studies were performed to evaluate the characteristics of this index. The approach is illustrated by a real data example.
resultsThe simulation studies revealed that the type I error rate is controlled. The approach can be applied in a superiority as well as in a non-inferiority setting.
conclusionsTest decisions can be based on the proposed index. The index may be a valuable complement to the commonly reported results of network meta-analyses. The method is easy to apply and of no (noticeable) additional computational cost.
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Registered trials
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.