ArticleBMC musculoskeletal disorders2024
Transitivity, coherence, and reliability of network meta-analyses comparing proximal humerus fracture treatments: a meta-epidemiological study.
Article in BMC musculoskeletal disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNetwork meta-analyses can be valuable for decision-makers in guiding clinical practice. However, for network meta-analysis results to be reliable, the assumptions of both transitivity and coherence must be met, and the methodology should adhere to current best practices. We aimed to assess whether network meta-analyses of randomized controlled trials (RCTs) comparing interventions for proximal humerus fractures provide reliable estimates of intervention effects.
methodsWe searched PubMed, EMBASE, The Cochrane Library, and Web of Science for network meta-analyses comparing interventions for proximal humerus fractures. We critically assessed the methodology regarding the development of a protocol, search strategy, trial inclusion, outcome extraction, and the methods used to conduct the network meta-analyses. We assessed the transitivity and coherence of the network graphs for the Constant score (CS), Disabilities of the Arm, Shoulder, and Hand score (DASH), and additional surgery. Transitivity was assessed by comparing probable effect modifiers (age, gender, fracture morphology, and comorbidities) across intervention comparisons. Coherence was assessed using Separating Indirect from Direct Evidence (SIDE) (Separating Indirect from Direct Evidence) and the design-by-treatment interaction test. We used CINeMA (Confidence in Network Meta-analyses) to assess the confidence in the results.
resultsNone of the three included network meta-analyses had a publicly available protocol or data-analysis plan, and they all had methodological flaws that could threaten the validity of their results. Although we did not detect incoherence for most comparisons, the transitivity assumption was violated for CS, DASH, and additional surgery in all three network meta-analyses. Additionally, the confidence in the results was 'very low' primarily due to within-study bias, reporting bias, intransitivity, imprecision, and heterogeneity.
conclusionsCurrent network meta-analyses of RCTs comparing interventions for proximal humerus fractures do not provide reliable estimates of intervention effects. We advise caution in using these network meta-analyses to guide clinical practice. To improve the utility of network meta-analyses to guide clinical practice, journal editors should require that network meta-analyses are done according to a predefined analysis plan in a publicly available protocol and that both coherence and transitivity have been adequately assessed and reported.
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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.