Evidence map›Paper›PMID 28806485›Full record

ArticleBiometrics2018

A matrix-based method of moments for fitting multivariate network meta-analysis models with multiple outcomes and random inconsistency effects.

Dan Jackson, Sylwia Bujkiewicz, Martin Law, Richard D Riley, Ian R White

Abstract read
In one paragraph

Article in Biometrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 8 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

25 citing papers in PubMed, 8 syntheses or guidelines pooled it.

  1. Pooled it
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  18. Classifying information-sharing methods.BMC medical research methodology · 2021
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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

5 authors.

Dan JacksonMRC Biostatistics Unit, Cambridge, U.K.ORCID 0000-0002-4963-8123
Sylwia BujkiewiczBiostatistics Research Group, Department of Health Sciences, University of Leicester, U.K.
Martin LawMRC Biostatistics Unit, Cambridge, U.K.
Richard D RileyCentre for Prognosis Research, Research Institute for Primary Care and Health Sciences, University of Keele, U.K.
Ian R WhiteMRC Biostatistics Unit, Cambridge, U.K.

Funding

Medical Research Council MC_U105260558Medical Research Council MC_UU_00002/3Medical Research Council MC_UU_12023/21Medical Research Council MR/J013595/1Medical Research Council MR/L009854/1
6 · The paper itself

Abstract

Random-effects meta-analyses are very commonly used in medical statistics. Recent methodological developments include multivariate (multiple outcomes) and network (multiple treatments) meta-analysis. Here, we provide a new model and corresponding estimation procedure for multivariate network meta-analysis, so that multiple outcomes and treatments can be included in a single analysis. Our new multivariate model is a direct extension of a univariate model for network meta-analysis that has recently been proposed. We allow two types of unknown variance parameters in our model, which represent between-study heterogeneity and inconsistency. Inconsistency arises when different forms of direct and indirect evidence are not in agreement, even having taken between-study heterogeneity into account. However, the consistency assumption is often assumed in practice and so we also explain how to fit a reduced model which makes this assumption. Our estimation method extends several other commonly used methods for meta-analysis, including the method proposed by DerSimonian and Laird (). We investigate the use of our proposed methods in the context of both a simulation study and a real example.

Indexed as

Models, StatisticalNetwork Meta-Analysis as TopicComputer SimulationMultivariate AnalysisIncoherenceMixed treatment comparisonsMultiple treatments meta-analysisRandom-effects models

Identifiers

PMID28806485
PMCPMC6038911

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Registered trials

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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.