Conduct and reporting of multivariate network meta-analyses: a scoping review
Bibliographic record
Abstract
OBJECTIVES: Combining multivariate and network meta-analysis methods simultaneously in a multivariate network meta-analysis (MVNMA) provides the methodological framework to analyze the largest amount of evidence relevant to decision-makers (i.e., from indirect evidence and correlated outcomes). The objectives of this scoping review were to summarize the characteristics of MVNMAs published in the health sciences literature and map the methodological guidance available for MVNMA. STUDY DESIGN AND SETTING: We searched MEDLINE, Embase, and the Cumulative Index to Nursing and Allied Health Literature from inception to 28 August 2023, along with citations of included studies, for quantitative evidence syntheses that applied MVNMA and articles addressing MVNMA methods. Pairs of reviewers independently screened potentially eligible studies. Collected data included bibliographic, methodological, and analytical characteristics of included studies. We reported results as total numbers, frequencies, and percentages for categorical variables and medians and interquartile ranges for continuous variables that were not normally distributed. RESULTS: After screening 1,075 titles and abstracts, and 112 full texts, we included 38 unique studies, of which, 10 were quantitative evidence syntheses that applied MVNMA and 28 were articles addressing MVNMA methods. Among the 10 MVNMAs, the first was published in 2013, four used studies identified from already published systematic reviews, and eight addressed pharmacological interventions, which were the most common interventions. They evaluated interventions for metastatic melanoma, colorectal cancer, prostate cancer, oral hygiene, disruptive behavior disorders, rheumatoid arthritis, narcolepsy, type 2 diabetes, and overactive bladder syndrome. Five MVNMAs analyzed two outcomes simultaneously, and four MVNMAs analyzed three outcomes simultaneously. Among the articles addressing MVNMA methods, the first was published in 2007 and the majority provided methodological frameworks for conducting MVNMAs (26/28, 93%). One study proposed criteria to standardize reporting of MVNMAs and two proposed items relevant to the quality assessment of MVNMAs. Study authors used data from 18 different illnesses to provide illustrative examples within their methodological guidance. CONCLUSIONS: The application of MVNMA in the health sciences literature is uncommon. Many methodological frameworks are published; however, standardization and specific criteria to guide reporting and quality assessment are lacking. This overview of the current landscape may help inform future conduct of MVNMAs and research on MVNMA methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.235 | 0.611 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.022 |
| Bibliometrics | 0.026 | 0.017 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".