Evidence map›Paper›PMID 23104435›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2013

Evidence synthesis for decision making 2: a generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials.

Sofia Dias, Alex J Sutton, A E Ades, Nicky J Welton

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 600 papers, 95 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
600citing papers in PubMed, 95 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

600 citing papers in PubMed, 95 syntheses or guidelines pooled it.

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540 more citing papers are in PubMed but not listed here.

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

4 authors.

Sofia DiasSchool of Social and Community Medicine, University of Bristol, Bristol, UK (SD, AEA, NJW)
Alex J SuttonDepartment of Health Sciences, University of Leicester, Leicester, UK (AJS)
A E AdesSchool of Social and Community Medicine, University of Bristol, Bristol, UK (SD, AEA, NJW)
Nicky J WeltonSchool of Social and Community Medicine, University of Bristol, Bristol, UK (SD, AEA, NJW)

Funding

Medical Research Council G0800800Medical Research Council G0802413Medical Research Council MC_U145079307
6 · The paper itself

Abstract

We set out a generalized linear model framework for the synthesis of data from randomized controlled trials. A common model is described, taking the form of a linear regression for both fixed and random effects synthesis, which can be implemented with normal, binomial, Poisson, and multinomial data. The familiar logistic model for meta-analysis with binomial data is a generalized linear model with a logit link function, which is appropriate for probability outcomes. The same linear regression framework can be applied to continuous outcomes, rate models, competing risks, or ordered category outcomes by using other link functions, such as identity, log, complementary log-log, and probit link functions. The common core model for the linear predictor can be applied to pairwise meta-analysis, indirect comparisons, synthesis of multiarm trials, and mixed treatment comparisons, also known as network meta-analysis, without distinction. We take a Bayesian approach to estimation and provide WinBUGS program code for a Bayesian analysis using Markov chain Monte Carlo simulation. An advantage of this approach is that it is straightforward to extend to shared parameter models where different randomized controlled trials report outcomes in different formats but from a common underlying model. Use of the generalized linear model framework allows us to present a unified account of how models can be compared using the deviance information criterion and how goodness of fit can be assessed using the residual deviance. The approach is illustrated through a range of worked examples for commonly encountered evidence formats.

Indexed as

Decision MakingEvidence-Based MedicineModels, TheoreticalLikelihood FunctionsNetwork Meta-Analysis as TopicRandomized Controlled Trials as Topicgeneralized linear modelindirect evidencemeta-analysisnetwork meta-analysis

Identifiers

PMID23104435
PMCPMC3704203

What OpenQuestion holds

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LicenceCC BY-NC
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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.