Evidence map›Paper›PMID 35883187›Full record

SynthesisSystematic reviews2022

Multivariate meta-analysis of multiple outcomes: characteristics and predictors of borrowing of strength from Cochrane reviews.

Miriam Hattle, Danielle L Burke, Thomas Trikalinos, Christopher H Schmid, Yong Chen, Dan Jackson, Richard D Riley

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Systematic reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
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  4. Pooled it
  5. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Miriam HattleCentre for Prognosis Research, School of Medicine, Keele University, Staffordshire, ST5 5BG, UK. m.s.hattle@keele.ac.uk.ORCID 0000-0003-1542-6277
Danielle L BurkeCentre for Prognosis Research, School of Medicine, Keele University, Staffordshire, ST5 5BG, UK.
Thomas TrikalinosDepartment of Biostatistics and Center for Evidence Synthesis in Health, Brown University School of Public Health, Providence, RI, 02912, USA.
Christopher H SchmidDepartment of Biostatistics and Center for Evidence Synthesis in Health, Brown University School of Public Health, Providence, RI, 02912, USA.
Yong ChenDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Dan JacksonStatistical Innovation, AstraZeneca, Academy House, 136 Hills Road, Cambridge, CB2 8PA, UK.
Richard D RileyCentre for Prognosis Research, School of Medicine, Keele University, Staffordshire, ST5 5BG, UK.

Funding

Dynamic learning for post-vaccine event prediction using temporal information in VAERSR01AI130460 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHEN, YONG, TAO, CUI · 2017 to 2021
$3.4M
A General Framework to Account for Outcome Reporting Bias in Systematic ReviewsR01LM012607 · NLM · UNIVERSITY OF PENNSYLVANIA · PI CHEN, YONG · 2017 to 2020
$1.4M
Department of Health 390Department of Health RP-PG-0615-20002NIAID NIH HHS 1R01AI130460NLM NIH HHS 1R01LM012607
6 · The paper itself

Abstract

objectivesMultivariate meta-analysis allows the joint synthesis of multiple outcomes accounting for their correlation. This enables borrowing of strength (BoS) across outcomes, which may lead to greater efficiency and even different conclusions compared to separate univariate meta-analyses. However, multivariate meta-analysis is complex to apply, so guidance is needed to flag (in advance of analysis) when the approach is most useful. STUDY DESIGN AND

settingWe use 43 Cochrane intervention reviews to empirically investigate the characteristics of meta-analysis datasets that are associated with a larger BoS statistic (from 0 to 100%) when applying a bivariate meta-analysis of binary outcomes.

resultsFour characteristics were identified as strongly associated with BoS: the total number of studies, the number of studies with the outcome of interest, the percentage of studies missing the outcome of interest, and the largest absolute within-study correlation. Using these characteristics, we then develop a model for predicting BoS in a new dataset, which is shown to have good performance (an adjusted R

conclusionsCochrane reviewers mainly use univariate meta-analysis methods, but the identified characteristics associated with BoS and our subsequent prediction model for BoS help to flag when a multivariate meta-analysis may also be beneficial in Cochrane reviews with multiple binary outcomes. Extension to non-Cochrane reviews and other outcome types is still required.

Indexed as

Research DesignHumansMultivariate AnalysisBorrowing of strengthIPD meta-analysisMeta-analysisMultivariate meta-analysis

Identifiers

PMID35883187
PMCPMC9316363

What OpenQuestion holds

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Registered trials

None linked

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.