Evidence map›Paper›PMID 31013000›Full record

ArticleResearch synthesis methods2019

Modelling time-course relationships with multiple treatments: Model-based network meta-analysis for continuous summary outcomes.

Hugo Pedder, Sofia Dias, Margherita Bennetts, Martin Boucher, Nicky J Welton

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Research synthesis methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 30 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed, 30 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

55 citing papers in PubMed, 30 syntheses or guidelines pooled it.

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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.

Hugo PedderDepartment of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.ORCID https://orcid.org/0000-0002-7813-3749
Sofia DiasDepartment of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.ORCID https://orcid.org/0000-0002-2172-0221
Margherita BennettsPharmacometrics, Pfizer Ltd, Kent, UK.ORCID https://orcid.org/0000-0001-5198-5274
Martin BoucherPharmacometrics, Pfizer Ltd, Kent, UK.
Nicky J WeltonDepartment of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.ORCID https://orcid.org/0000-0003-2198-3205

Funding

Medical Research Council MR/K025643/1Medical Research Council MR/M005232/1Medical Research Council MR/M005615/1MRC ConDuCT-II Hub for Trials Methodology Research MR/K025643/1MRC Methodology Research Programme MR/M005615/1MRC New Investigator Research Grant MR/M005232/1National Institute for Health Research Biomedical Centre at the University Hospitals Bristol NHS Foundation TrustPfizerPfizer LtdUniversity of Bristol
6 · The paper itself

Abstract

backgroundModel-based meta-analysis (MBMA) is increasingly used to inform drug-development decisions by synthesising results from multiple studies to estimate treatment, dose-response, and time-course characteristics. Network meta-analysis (NMA) is used in Health Technology Appraisals for simultaneously comparing effects of multiple treatments, to inform reimbursement decisions. Recently, a framework for dose-response model-based network meta-analysis (MBNMA) has been proposed that combines, often nonlinear, MBMA modelling with the statistically robust properties of NMA. Here, we aim to extend this framework to time-course models.

methodsWe propose a Bayesian time-course MBNMA modelling framework for continuous summary outcomes that allows for nonlinear modelling of multiparameter time-course functions, accounts for residual correlation between observations, preserves randomisation by modelling relative effects, and allows for testing of inconsistency between direct and indirect evidence on the time-course parameters. We demonstrate our modelling framework using an illustrative dataset of 23 trials investigating treatments for pain in osteoarthritis.

resultsOf the time-course functions that we explored, the E

conclusionsTime-course MBNMA provides a statistically robust framework for synthesising evidence on multiple treatments at multiple time points. The use of placebo-controlled studies in drug-development means there is limited potential for inconsistency. The methods can inform drug-development decisions and provide the rigour needed in the reimbursement decision-making process.

Indexed as

Treatment OutcomeBayes TheoremBiomedical TechnologyClinical Trials as TopicHumansLinear ModelsNetwork Meta-Analysis as TopicOsteoarthritisPain ManagementReproducibility of Results

Identifiers

PMID31013000
PMCPMC6563489

What OpenQuestion holds

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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.